{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "os.environ[\"CUDA_DEVICE_ORDER\"] = 'PCI_BUS_ID'\n",
    "os.environ[\"CUDA_VISIBLE_DEVICES\"] = '0,3,6,7'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import sys\n",
    "# sys.path.insert(0, '/net/per920a/export/das14a/satoh-lab/shiv25/spot_fake/godin_files/')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "3a4da0d6eda541868c5b831db22c539d",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(IntProgress(value=1, bar_style='info', max=1), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING: Logging before flag parsing goes to stderr.\n",
      "W0603 14:45:59.656936 140683784238912 __init__.py:56] Some hub symbols are not available because TensorFlow version is less than 1.14\n",
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from tqdm import tqdm_notebook as tqdm\n",
    "tqdm().pandas()\n",
    "import re\n",
    "import string\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "import cv2\n",
    "from os import listdir\n",
    "from sklearn.metrics import accuracy_score\n",
    "\n",
    "import tensorflow as tf\n",
    "import tensorflow_hub as hub  #pip install tensorflow_hub\n",
    "import os\n",
    "from tokenization import FullTokenizer\n",
    "from tqdm import tqdm_notebook\n",
    "from tensorflow.keras import backend as K\n",
    "# from keras import backend as K\n",
    "# Initialize session\n",
    "# sess = tf.Session()\n",
    "sess = tf.Session(config=tf.ConfigProto(allow_soft_placement=True, log_device_placement=True)) #command to run codeon multiple gpu\n",
    "\n",
    "\n",
    "\n",
    "# Params for bert model and tokenization\n",
    "bert_path = \"https://tfhub.dev/google/bert_uncased_L-12_H-768_A-12/1\"\n",
    "\n",
    "from keras_lr_finder import LRFinder\n",
    "import talos as ta\n",
    "from pprint import pprint\n",
    "from talos import live\n",
    "tf.logging.set_verbosity(tf.logging.ERROR)\n",
    "from random import choice\n",
    "import gc\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [],
   "source": [
    "from talos.model.normalizers import lr_normalizer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.random.seed(42)\n",
    "tf.set_random_seed(42)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Read data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ls: cannot access '../dataset/twitter_final/mediaeval2016/': No such file or directory\r\n"
     ]
    }
   ],
   "source": [
    "!ls ../dataset/twitter_final/mediaeval2016/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_df(file):\n",
    "    return pd.read_csv(file,sep = '\\t')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_df = get_df('dataset/twitter_final/mediaeval2016/train_posts.txt')\n",
    "test_df = get_df('dataset/twitter_final/mediaeval2016/test_posts.txt')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "def return_first_image(row):\n",
    "    return row['image_id(s)'].split(',')[0].strip()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "85045db623c84b42825b5239d8d97720",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(IntProgress(value=0, max=15629), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "train_df['first_image_id'] = train_df.progress_apply (lambda row: return_first_image(row),axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>post_id</th>\n",
       "      <th>post_text</th>\n",
       "      <th>user_id</th>\n",
       "      <th>image_id(s)</th>\n",
       "      <th>username</th>\n",
       "      <th>timestamp</th>\n",
       "      <th>label</th>\n",
       "      <th>first_image_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>324597532548276224</td>\n",
       "      <td>Don't need feds to solve the #bostonbombing wh...</td>\n",
       "      <td>886672620</td>\n",
       "      <td>boston_fake_03,boston_fake_35</td>\n",
       "      <td>SantaCruzShred</td>\n",
       "      <td>Wed Apr 17 18:57:37 +0000 2013</td>\n",
       "      <td>fake</td>\n",
       "      <td>boston_fake_03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>325145334739267584</td>\n",
       "      <td>PIC: Comparison of #Boston suspect Sunil Tripa...</td>\n",
       "      <td>21992286</td>\n",
       "      <td>boston_fake_23</td>\n",
       "      <td>Oscar_Wang</td>\n",
       "      <td>Fri Apr 19 07:14:23 +0000 2013</td>\n",
       "      <td>fake</td>\n",
       "      <td>boston_fake_23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>325152091423248385</td>\n",
       "      <td>I'm not completely convinced that it's this Su...</td>\n",
       "      <td>16428755</td>\n",
       "      <td>boston_fake_34</td>\n",
       "      <td>jamwil</td>\n",
       "      <td>Fri Apr 19 07:41:14 +0000 2013</td>\n",
       "      <td>fake</td>\n",
       "      <td>boston_fake_34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>324554646976868352</td>\n",
       "      <td>Brutal lo que se puede conseguir en colaboraci...</td>\n",
       "      <td>303138574</td>\n",
       "      <td>boston_fake_03,boston_fake_35</td>\n",
       "      <td>rubenson80</td>\n",
       "      <td>Wed Apr 17 16:07:12 +0000 2013</td>\n",
       "      <td>fake</td>\n",
       "      <td>boston_fake_03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>324315545572896768</td>\n",
       "      <td>4chan and the bombing. just throwing it out th...</td>\n",
       "      <td>180460772</td>\n",
       "      <td>boston_fake_15</td>\n",
       "      <td>Slimlenny</td>\n",
       "      <td>Wed Apr 17 00:17:06 +0000 2013</td>\n",
       "      <td>fake</td>\n",
       "      <td>boston_fake_15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>324581777614180352</td>\n",
       "      <td>4chan thinks they found pictures of the bomber...</td>\n",
       "      <td>46224814</td>\n",
       "      <td>boston_fake_08</td>\n",
       "      <td>iamyadvinder</td>\n",
       "      <td>Wed Apr 17 17:55:00 +0000 2013</td>\n",
       "      <td>fake</td>\n",
       "      <td>boston_fake_08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>324665423956176896</td>\n",
       "      <td>Ola ke ase, investigando las bombas de Boston ...</td>\n",
       "      <td>90735851</td>\n",
       "      <td>boston_fake_35</td>\n",
       "      <td>rcr866</td>\n",
       "      <td>Wed Apr 17 23:27:23 +0000 2013</td>\n",
       "      <td>fake</td>\n",
       "      <td>boston_fake_35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>325464125868216321</td>\n",
       "      <td>4chan ThinkTank - Imgur http://t.co/hQt2fhxE48</td>\n",
       "      <td>142785938</td>\n",
       "      <td>boston_fake_03,boston_fake_35</td>\n",
       "      <td>GlebGgs</td>\n",
       "      <td>Sat Apr 20 04:21:09 +0000 2013</td>\n",
       "      <td>fake</td>\n",
       "      <td>boston_fake_03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>325099014355820544</td>\n",
       "      <td>@DLoesch have you seen this? Bomber #2  looks ...</td>\n",
       "      <td>21769179</td>\n",
       "      <td>boston_fake_13</td>\n",
       "      <td>larrygloverlive</td>\n",
       "      <td>Fri Apr 19 04:10:19 +0000 2013</td>\n",
       "      <td>fake</td>\n",
       "      <td>boston_fake_13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>325705653026975744</td>\n",
       "      <td>da 4chan think tank BOSTON  http://t.co/0ZbKMA...</td>\n",
       "      <td>51410043</td>\n",
       "      <td>boston_fake_03,boston_fake_35</td>\n",
       "      <td>IsraelTucker</td>\n",
       "      <td>Sat Apr 20 20:20:53 +0000 2013</td>\n",
       "      <td>fake</td>\n",
       "      <td>boston_fake_03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>324637464927039488</td>\n",
       "      <td>@Anonymoussops @bernardosampa @urbanohumano de...</td>\n",
       "      <td>224438064</td>\n",
       "      <td>boston_fake_03,boston_fake_35</td>\n",
       "      <td>MrDDS1984</td>\n",
       "      <td>Wed Apr 17 21:36:17 +0000 2013</td>\n",
       "      <td>fake</td>\n",
       "      <td>boston_fake_03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>325101127043534850</td>\n",
       "      <td>Poo, o 4CHAN nas análises dos atentados hein. ...</td>\n",
       "      <td>97034316</td>\n",
       "      <td>boston_fake_03,boston_fake_35</td>\n",
       "      <td>AriellMarques</td>\n",
       "      <td>Fri Apr 19 04:18:43 +0000 2013</td>\n",
       "      <td>fake</td>\n",
       "      <td>boston_fake_03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>324498279700234240</td>\n",
       "      <td>@DandCShow Did you guys see these pics?  http:...</td>\n",
       "      <td>21324077</td>\n",
       "      <td>boston_fake_03,boston_fake_35</td>\n",
       "      <td>SignoreDago</td>\n",
       "      <td>Wed Apr 17 12:23:13 +0000 2013</td>\n",
       "      <td>fake</td>\n",
       "      <td>boston_fake_03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>324604457847488512</td>\n",
       "      <td>http://t.co/ykSVPMWD6h  #4chan FTW</td>\n",
       "      <td>201914406</td>\n",
       "      <td>boston_fake_03,boston_fake_35</td>\n",
       "      <td>mtdisme</td>\n",
       "      <td>Wed Apr 17 19:25:08 +0000 2013</td>\n",
       "      <td>fake</td>\n",
       "      <td>boston_fake_03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>324580144478048258</td>\n",
       "      <td>Wow, creepy. #Boston #Bombing ID the suspect? ...</td>\n",
       "      <td>114819961</td>\n",
       "      <td>boston_fake_03,boston_fake_35</td>\n",
       "      <td>JaclynIsaac</td>\n",
       "      <td>Wed Apr 17 17:48:31 +0000 2013</td>\n",
       "      <td>fake</td>\n",
       "      <td>boston_fake_03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>324558285225209857</td>\n",
       "      <td>@YourAnonNews its not that guy, you can see hi...</td>\n",
       "      <td>50858723</td>\n",
       "      <td>boston_fake_16</td>\n",
       "      <td>Tadlette</td>\n",
       "      <td>Wed Apr 17 16:21:39 +0000 2013</td>\n",
       "      <td>fake</td>\n",
       "      <td>boston_fake_16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>324510098259050496</td>\n",
       "      <td>@BBCWorld @BBCBreaking @BBCNews Probably worth...</td>\n",
       "      <td>241810080</td>\n",
       "      <td>boston_fake_03,boston_fake_35</td>\n",
       "      <td>FaultyBeard</td>\n",
       "      <td>Wed Apr 17 13:10:11 +0000 2013</td>\n",
       "      <td>fake</td>\n",
       "      <td>boston_fake_03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>324635274887970816</td>\n",
       "      <td>my best guess ala 4chan regarding Boston \\nhtt...</td>\n",
       "      <td>109661488</td>\n",
       "      <td>boston_fake_03,boston_fake_07,boston_fake_35</td>\n",
       "      <td>SOS3Today</td>\n",
       "      <td>Wed Apr 17 21:27:35 +0000 2013</td>\n",
       "      <td>fake</td>\n",
       "      <td>boston_fake_03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>324435205433724929</td>\n",
       "      <td>Hilarious if 4chan solves this bombing case. T...</td>\n",
       "      <td>16037772</td>\n",
       "      <td>boston_fake_26</td>\n",
       "      <td>BlakeYo</td>\n",
       "      <td>Wed Apr 17 08:12:35 +0000 2013</td>\n",
       "      <td>fake</td>\n",
       "      <td>boston_fake_26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>324545148908363777</td>\n",
       "      <td>http://t.co/pS4KetWGaW #4chan</td>\n",
       "      <td>396729812</td>\n",
       "      <td>boston_fake_01</td>\n",
       "      <td>RutgerDrent</td>\n",
       "      <td>Wed Apr 17 15:29:27 +0000 2013</td>\n",
       "      <td>fake</td>\n",
       "      <td>boston_fake_01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>324267996044460035</td>\n",
       "      <td>\\\"They say\\\" this is a #pic of a man lurking o...</td>\n",
       "      <td>266869357</td>\n",
       "      <td>boston_fake_09</td>\n",
       "      <td>broadwaybeezy</td>\n",
       "      <td>Tue Apr 16 21:08:09 +0000 2013</td>\n",
       "      <td>fake</td>\n",
       "      <td>boston_fake_09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>324513597852098561</td>\n",
       "      <td>Wow 4chan. That was fast: http://t.co/3vHkPCr8tR</td>\n",
       "      <td>363958072</td>\n",
       "      <td>boston_fake_15</td>\n",
       "      <td>Alpha_and_Delta</td>\n",
       "      <td>Wed Apr 17 13:24:05 +0000 2013</td>\n",
       "      <td>fake</td>\n",
       "      <td>boston_fake_15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>324979270248173568</td>\n",
       "      <td>#4Chan got some fame on internet but I can say...</td>\n",
       "      <td>608583</td>\n",
       "      <td>boston_fake_03,boston_fake_35</td>\n",
       "      <td>Zeinobia</td>\n",
       "      <td>Thu Apr 18 20:14:30 +0000 2013</td>\n",
       "      <td>fake</td>\n",
       "      <td>boston_fake_03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>325064824306429953</td>\n",
       "      <td>4chan melhor que FBI :) http://t.co/aiGcdBXRUz</td>\n",
       "      <td>323637124</td>\n",
       "      <td>boston_fake_03,boston_fake_35</td>\n",
       "      <td>bruunowtf</td>\n",
       "      <td>Fri Apr 19 01:54:28 +0000 2013</td>\n",
       "      <td>fake</td>\n",
       "      <td>boston_fake_03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>324333359356579841</td>\n",
       "      <td>4chan spots a #Boston Bombing suspect - http:/...</td>\n",
       "      <td>105148135</td>\n",
       "      <td>boston_fake_15</td>\n",
       "      <td>giggitygreg</td>\n",
       "      <td>Wed Apr 17 01:27:53 +0000 2013</td>\n",
       "      <td>fake</td>\n",
       "      <td>boston_fake_15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>324631982518726656</td>\n",
       "      <td>4Chan seems to think they've found one of the ...</td>\n",
       "      <td>419544419</td>\n",
       "      <td>boston_fake_29</td>\n",
       "      <td>smitchell550</td>\n",
       "      <td>Wed Apr 17 21:14:30 +0000 2013</td>\n",
       "      <td>fake</td>\n",
       "      <td>boston_fake_29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>324356688272560128</td>\n",
       "      <td>4chan spots a suspect http://t.co/u3Ovlyl4Tp [...</td>\n",
       "      <td>1228761494</td>\n",
       "      <td>boston_fake_15</td>\n",
       "      <td>Reddit4Savages</td>\n",
       "      <td>Wed Apr 17 03:00:35 +0000 2013</td>\n",
       "      <td>fake</td>\n",
       "      <td>boston_fake_15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>324874410374877185</td>\n",
       "      <td>http://t.co/TTcLPjXHVs investigación bombas Bo...</td>\n",
       "      <td>348687483</td>\n",
       "      <td>boston_fake_03,boston_fake_35</td>\n",
       "      <td>isaponferrada</td>\n",
       "      <td>Thu Apr 18 13:17:49 +0000 2013</td>\n",
       "      <td>fake</td>\n",
       "      <td>boston_fake_03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>324569472952320003</td>\n",
       "      <td>#4chan solves the bombings? http://t.co/YeTDt6...</td>\n",
       "      <td>394713293</td>\n",
       "      <td>boston_fake_03,boston_fake_35</td>\n",
       "      <td>AnssiMuhonen</td>\n",
       "      <td>Wed Apr 17 17:06:07 +0000 2013</td>\n",
       "      <td>fake</td>\n",
       "      <td>boston_fake_03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>324936511160213505</td>\n",
       "      <td>4chan Conspiracies 1 of 2 http://t.co/KbYZr9aAiQ</td>\n",
       "      <td>142869850</td>\n",
       "      <td>boston_fake_18</td>\n",
       "      <td>brennen_says</td>\n",
       "      <td>Thu Apr 18 17:24:35 +0000 2013</td>\n",
       "      <td>fake</td>\n",
       "      <td>boston_fake_18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15599</th>\n",
       "      <td>578520013830672386</td>\n",
       "      <td>#nosnieuws ZDF geeft toe: middelvinger Varoufa...</td>\n",
       "      <td>2772819884</td>\n",
       "      <td>varoufakis_1</td>\n",
       "      <td>denieuwsvolger</td>\n",
       "      <td>Thu Mar 19 11:34:54 +0000 2015</td>\n",
       "      <td>fake</td>\n",
       "      <td>varoufakis_1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15600</th>\n",
       "      <td>578520029387313153</td>\n",
       "      <td>#nieuws ZDF geeft toe: middelvinger Varoufakis...</td>\n",
       "      <td>788181799</td>\n",
       "      <td>varoufakis_1</td>\n",
       "      <td>nieuws_actueel</td>\n",
       "      <td>Thu Mar 19 11:34:58 +0000 2015</td>\n",
       "      <td>fake</td>\n",
       "      <td>varoufakis_1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15601</th>\n",
       "      <td>578520104234778624</td>\n",
       "      <td>ZDF geeft toe: middelvinger Varoufakis toch ec...</td>\n",
       "      <td>9623582</td>\n",
       "      <td>varoufakis_1</td>\n",
       "      <td>jcmraats</td>\n",
       "      <td>Thu Mar 19 11:35:15 +0000 2015</td>\n",
       "      <td>fake</td>\n",
       "      <td>varoufakis_1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15602</th>\n",
       "      <td>578520179140894720</td>\n",
       "      <td>NOS | ZDF geeft toe: middelvinger Varoufakis t...</td>\n",
       "      <td>1304420018</td>\n",
       "      <td>varoufakis_1</td>\n",
       "      <td>Kranten_en_RTV</td>\n",
       "      <td>Thu Mar 19 11:35:33 +0000 2015</td>\n",
       "      <td>fake</td>\n",
       "      <td>varoufakis_1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15603</th>\n",
       "      <td>578520181972025344</td>\n",
       "      <td>ZDF geeft toe: middelvinger Varoufakis toch ec...</td>\n",
       "      <td>115913059</td>\n",
       "      <td>varoufakis_1</td>\n",
       "      <td>hardecijfers</td>\n",
       "      <td>Thu Mar 19 11:35:34 +0000 2015</td>\n",
       "      <td>fake</td>\n",
       "      <td>varoufakis_1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15604</th>\n",
       "      <td>578520316525166593</td>\n",
       "      <td>ZDF geeft toe: middelvinger Varoufakis toch ec...</td>\n",
       "      <td>591836976</td>\n",
       "      <td>varoufakis_1</td>\n",
       "      <td>vosinfo</td>\n",
       "      <td>Thu Mar 19 11:36:06 +0000 2015</td>\n",
       "      <td>fake</td>\n",
       "      <td>varoufakis_1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15605</th>\n",
       "      <td>578520323269599233</td>\n",
       "      <td>ZDF geeft toe: middelvinger Varoufakis toch ec...</td>\n",
       "      <td>1098982633</td>\n",
       "      <td>varoufakis_1</td>\n",
       "      <td>wowobbel</td>\n",
       "      <td>Thu Mar 19 11:36:08 +0000 2015</td>\n",
       "      <td>fake</td>\n",
       "      <td>varoufakis_1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15606</th>\n",
       "      <td>578520375635525632</td>\n",
       "      <td>[NOS] ZDF geeft toe: middelvinger Varoufakis t...</td>\n",
       "      <td>592927998</td>\n",
       "      <td>varoufakis_1</td>\n",
       "      <td>Message_11</td>\n",
       "      <td>Thu Mar 19 11:36:20 +0000 2015</td>\n",
       "      <td>fake</td>\n",
       "      <td>varoufakis_1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15607</th>\n",
       "      <td>578524169110958080</td>\n",
       "      <td>ZDF geeft toe: middelvinger Varoufakis toch ec...</td>\n",
       "      <td>865194175</td>\n",
       "      <td>varoufakis_1</td>\n",
       "      <td>EricdeBruin4</td>\n",
       "      <td>Thu Mar 19 11:51:25 +0000 2015</td>\n",
       "      <td>fake</td>\n",
       "      <td>varoufakis_1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15608</th>\n",
       "      <td>578537166579478528</td>\n",
       "      <td>ZDF klärt auf: \\\"Varoufake-Video ist Satire\\\":...</td>\n",
       "      <td>188246428</td>\n",
       "      <td>varoufakis_1</td>\n",
       "      <td>fn_unterhaltung</td>\n",
       "      <td>Thu Mar 19 12:43:03 +0000 2015</td>\n",
       "      <td>fake</td>\n",
       "      <td>varoufakis_1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15609</th>\n",
       "      <td>578537359710556160</td>\n",
       "      <td>ZDF klärt auf: \\\"Varoufake-Video ist Satire\\\":...</td>\n",
       "      <td>27620848</td>\n",
       "      <td>varoufakis_1</td>\n",
       "      <td>freenet_auto</td>\n",
       "      <td>Thu Mar 19 12:43:49 +0000 2015</td>\n",
       "      <td>fake</td>\n",
       "      <td>varoufakis_1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15610</th>\n",
       "      <td>578525401896628224</td>\n",
       "      <td>ZDF beteuert: Böhmermanns Varoufakis-Video war...</td>\n",
       "      <td>19026329</td>\n",
       "      <td>varoufakis_1</td>\n",
       "      <td>NZ_Online</td>\n",
       "      <td>Thu Mar 19 11:56:18 +0000 2015</td>\n",
       "      <td>fake</td>\n",
       "      <td>varoufakis_1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15611</th>\n",
       "      <td>578526075308871680</td>\n",
       "      <td>Deutsche Medien Lächerlich _ZDF klärt auf: Böh...</td>\n",
       "      <td>139368676</td>\n",
       "      <td>varoufakis_1</td>\n",
       "      <td>barkovskymedia</td>\n",
       "      <td>Thu Mar 19 11:58:59 +0000 2015</td>\n",
       "      <td>fake</td>\n",
       "      <td>varoufakis_1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15612</th>\n",
       "      <td>578527634600013825</td>\n",
       "      <td>ZDF geeft toe: middelvinger Varoufakis toch ec...</td>\n",
       "      <td>1308717630</td>\n",
       "      <td>varoufakis_1</td>\n",
       "      <td>spanjevandaagnl</td>\n",
       "      <td>Thu Mar 19 12:05:11 +0000 2015</td>\n",
       "      <td>fake</td>\n",
       "      <td>varoufakis_1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15613</th>\n",
       "      <td>578527817056550912</td>\n",
       "      <td>p2000 update ZDF geeft toe: middelvinger Varou...</td>\n",
       "      <td>580678766</td>\n",
       "      <td>varoufakis_1</td>\n",
       "      <td>KORTNEWSNL</td>\n",
       "      <td>Thu Mar 19 12:05:54 +0000 2015</td>\n",
       "      <td>fake</td>\n",
       "      <td>varoufakis_1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15614</th>\n",
       "      <td>578551854491119616</td>\n",
       "      <td>Tot echt !! ttp://nos.nl/artikel/2025694-zdf-g...</td>\n",
       "      <td>3070706739</td>\n",
       "      <td>varoufakis_1</td>\n",
       "      <td>Chrisaport</td>\n",
       "      <td>Thu Mar 19 13:41:25 +0000 2015</td>\n",
       "      <td>fake</td>\n",
       "      <td>varoufakis_1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15615</th>\n",
       "      <td>578560339823955968</td>\n",
       "      <td>Stinkefinger: ZDF erklärt Böhmermanns Varoufak...</td>\n",
       "      <td>107723722</td>\n",
       "      <td>varoufakis_1</td>\n",
       "      <td>Harald_Bulling</td>\n",
       "      <td>Thu Mar 19 14:15:08 +0000 2015</td>\n",
       "      <td>fake</td>\n",
       "      <td>varoufakis_1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15616</th>\n",
       "      <td>578565621408989184</td>\n",
       "      <td>ZDF erklärt Böhmermanns Varoufakis-Coup zur Sa...</td>\n",
       "      <td>636243442</td>\n",
       "      <td>varoufakis_1</td>\n",
       "      <td>LyingInTheDark</td>\n",
       "      <td>Thu Mar 19 14:36:08 +0000 2015</td>\n",
       "      <td>fake</td>\n",
       "      <td>varoufakis_1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15617</th>\n",
       "      <td>578597197782061056</td>\n",
       "      <td>Top story: Jan Böhmermanns Varoufakis-Film ist...</td>\n",
       "      <td>1600725278</td>\n",
       "      <td>varoufakis_1</td>\n",
       "      <td>AutomobileGabri</td>\n",
       "      <td>Thu Mar 19 16:41:36 +0000 2015</td>\n",
       "      <td>fake</td>\n",
       "      <td>varoufakis_1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15618</th>\n",
       "      <td>578636536956194816</td>\n",
       "      <td>Jan Böhmermanns Varoufakis-Film ist laut ZDF F...</td>\n",
       "      <td>44852901</td>\n",
       "      <td>varoufakis_1</td>\n",
       "      <td>schmitz_bkmks</td>\n",
       "      <td>Thu Mar 19 19:17:55 +0000 2015</td>\n",
       "      <td>fake</td>\n",
       "      <td>varoufakis_1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15619</th>\n",
       "      <td>578696262683357185</td>\n",
       "      <td>Riesen Verwirrung um Varoufakis-Video ZDF-Come...</td>\n",
       "      <td>390148936</td>\n",
       "      <td>varoufakis_1</td>\n",
       "      <td>MatthieuMARIA</td>\n",
       "      <td>Thu Mar 19 23:15:15 +0000 2015</td>\n",
       "      <td>fake</td>\n",
       "      <td>varoufakis_1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15620</th>\n",
       "      <td>580328983503466496</td>\n",
       "      <td>#Stinkefinger von Varoufakis: Böhmermann ZDF #...</td>\n",
       "      <td>2783698124</td>\n",
       "      <td>varoufakis_1</td>\n",
       "      <td>TopLinke</td>\n",
       "      <td>Tue Mar 24 11:23:06 +0000 2015</td>\n",
       "      <td>fake</td>\n",
       "      <td>varoufakis_1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15621</th>\n",
       "      <td>580330741931909120</td>\n",
       "      <td>#Stinkefinger von Varoufakis: Böhmermann ZDF #...</td>\n",
       "      <td>2362990682</td>\n",
       "      <td>varoufakis_1</td>\n",
       "      <td>DieLinkeKVKNO</td>\n",
       "      <td>Tue Mar 24 11:30:05 +0000 2015</td>\n",
       "      <td>fake</td>\n",
       "      <td>varoufakis_1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15622</th>\n",
       "      <td>578297874896805891</td>\n",
       "      <td>ZDF Neo lays into #jauch about the Varoufakis ...</td>\n",
       "      <td>17442320</td>\n",
       "      <td>varoufakis_1</td>\n",
       "      <td>jonworth</td>\n",
       "      <td>Wed Mar 18 20:52:12 +0000 2015</td>\n",
       "      <td>fake</td>\n",
       "      <td>varoufakis_1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15623</th>\n",
       "      <td>578358505779793920</td>\n",
       "      <td>ZDF Neo : Der Stinkefinger von Varoufakis - Do...</td>\n",
       "      <td>19240255</td>\n",
       "      <td>varoufakis_1</td>\n",
       "      <td>morgenpost</td>\n",
       "      <td>Thu Mar 19 00:53:07 +0000 2015</td>\n",
       "      <td>fake</td>\n",
       "      <td>varoufakis_1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15624</th>\n",
       "      <td>578433150071775232</td>\n",
       "      <td>Un présentateur de la ZDF confesse avoir truqu...</td>\n",
       "      <td>257551211</td>\n",
       "      <td>varoufakis_1</td>\n",
       "      <td>Cdt_Sylvestre</td>\n",
       "      <td>Thu Mar 19 05:49:44 +0000 2015</td>\n",
       "      <td>fake</td>\n",
       "      <td>varoufakis_1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15625</th>\n",
       "      <td>578433646597656576</td>\n",
       "      <td>Oh les kleine menteurs \\\"@CorineBarella: Un pr...</td>\n",
       "      <td>27575883</td>\n",
       "      <td>varoufakis_1</td>\n",
       "      <td>damomarc</td>\n",
       "      <td>Thu Mar 19 05:51:42 +0000 2015</td>\n",
       "      <td>fake</td>\n",
       "      <td>varoufakis_1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15626</th>\n",
       "      <td>578486910491996160</td>\n",
       "      <td>Este es el programa de ZDF en el que confirman...</td>\n",
       "      <td>2049211</td>\n",
       "      <td>varoufakis_1</td>\n",
       "      <td>javierpascual</td>\n",
       "      <td>Thu Mar 19 09:23:21 +0000 2015</td>\n",
       "      <td>fake</td>\n",
       "      <td>varoufakis_1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15627</th>\n",
       "      <td>578505023912591360</td>\n",
       "      <td>11.34 - wir haben FAST Mittag ▶ Riesen Verwirr...</td>\n",
       "      <td>262222386</td>\n",
       "      <td>varoufakis_1</td>\n",
       "      <td>aotto1968_2</td>\n",
       "      <td>Thu Mar 19 10:35:20 +0000 2015</td>\n",
       "      <td>fake</td>\n",
       "      <td>varoufakis_1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15628</th>\n",
       "      <td>578305144380612609</td>\n",
       "      <td>Sorry, @yanisvaroufakis! https://t.co/BSkYrbII...</td>\n",
       "      <td>19072286</td>\n",
       "      <td>varoufakis_1</td>\n",
       "      <td>janboehm</td>\n",
       "      <td>Wed Mar 18 21:21:05 +0000 2015</td>\n",
       "      <td>fake</td>\n",
       "      <td>varoufakis_1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>15629 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                  post_id                                          post_text  \\\n",
       "0      324597532548276224  Don't need feds to solve the #bostonbombing wh...   \n",
       "1      325145334739267584  PIC: Comparison of #Boston suspect Sunil Tripa...   \n",
       "2      325152091423248385  I'm not completely convinced that it's this Su...   \n",
       "3      324554646976868352  Brutal lo que se puede conseguir en colaboraci...   \n",
       "4      324315545572896768  4chan and the bombing. just throwing it out th...   \n",
       "5      324581777614180352  4chan thinks they found pictures of the bomber...   \n",
       "6      324665423956176896  Ola ke ase, investigando las bombas de Boston ...   \n",
       "7      325464125868216321     4chan ThinkTank - Imgur http://t.co/hQt2fhxE48   \n",
       "8      325099014355820544  @DLoesch have you seen this? Bomber #2  looks ...   \n",
       "9      325705653026975744  da 4chan think tank BOSTON  http://t.co/0ZbKMA...   \n",
       "10     324637464927039488  @Anonymoussops @bernardosampa @urbanohumano de...   \n",
       "11     325101127043534850  Poo, o 4CHAN nas análises dos atentados hein. ...   \n",
       "12     324498279700234240  @DandCShow Did you guys see these pics?  http:...   \n",
       "13     324604457847488512                 http://t.co/ykSVPMWD6h  #4chan FTW   \n",
       "14     324580144478048258  Wow, creepy. #Boston #Bombing ID the suspect? ...   \n",
       "15     324558285225209857  @YourAnonNews its not that guy, you can see hi...   \n",
       "16     324510098259050496  @BBCWorld @BBCBreaking @BBCNews Probably worth...   \n",
       "17     324635274887970816  my best guess ala 4chan regarding Boston \\nhtt...   \n",
       "18     324435205433724929  Hilarious if 4chan solves this bombing case. T...   \n",
       "19     324545148908363777                      http://t.co/pS4KetWGaW #4chan   \n",
       "20     324267996044460035  \\\"They say\\\" this is a #pic of a man lurking o...   \n",
       "21     324513597852098561   Wow 4chan. That was fast: http://t.co/3vHkPCr8tR   \n",
       "22     324979270248173568  #4Chan got some fame on internet but I can say...   \n",
       "23     325064824306429953     4chan melhor que FBI :) http://t.co/aiGcdBXRUz   \n",
       "24     324333359356579841  4chan spots a #Boston Bombing suspect - http:/...   \n",
       "25     324631982518726656  4Chan seems to think they've found one of the ...   \n",
       "26     324356688272560128  4chan spots a suspect http://t.co/u3Ovlyl4Tp [...   \n",
       "27     324874410374877185  http://t.co/TTcLPjXHVs investigación bombas Bo...   \n",
       "28     324569472952320003  #4chan solves the bombings? http://t.co/YeTDt6...   \n",
       "29     324936511160213505   4chan Conspiracies 1 of 2 http://t.co/KbYZr9aAiQ   \n",
       "...                   ...                                                ...   \n",
       "15599  578520013830672386  #nosnieuws ZDF geeft toe: middelvinger Varoufa...   \n",
       "15600  578520029387313153  #nieuws ZDF geeft toe: middelvinger Varoufakis...   \n",
       "15601  578520104234778624  ZDF geeft toe: middelvinger Varoufakis toch ec...   \n",
       "15602  578520179140894720  NOS | ZDF geeft toe: middelvinger Varoufakis t...   \n",
       "15603  578520181972025344  ZDF geeft toe: middelvinger Varoufakis toch ec...   \n",
       "15604  578520316525166593  ZDF geeft toe: middelvinger Varoufakis toch ec...   \n",
       "15605  578520323269599233  ZDF geeft toe: middelvinger Varoufakis toch ec...   \n",
       "15606  578520375635525632  [NOS] ZDF geeft toe: middelvinger Varoufakis t...   \n",
       "15607  578524169110958080  ZDF geeft toe: middelvinger Varoufakis toch ec...   \n",
       "15608  578537166579478528  ZDF klärt auf: \\\"Varoufake-Video ist Satire\\\":...   \n",
       "15609  578537359710556160  ZDF klärt auf: \\\"Varoufake-Video ist Satire\\\":...   \n",
       "15610  578525401896628224  ZDF beteuert: Böhmermanns Varoufakis-Video war...   \n",
       "15611  578526075308871680  Deutsche Medien Lächerlich _ZDF klärt auf: Böh...   \n",
       "15612  578527634600013825  ZDF geeft toe: middelvinger Varoufakis toch ec...   \n",
       "15613  578527817056550912  p2000 update ZDF geeft toe: middelvinger Varou...   \n",
       "15614  578551854491119616  Tot echt !! ttp://nos.nl/artikel/2025694-zdf-g...   \n",
       "15615  578560339823955968  Stinkefinger: ZDF erklärt Böhmermanns Varoufak...   \n",
       "15616  578565621408989184  ZDF erklärt Böhmermanns Varoufakis-Coup zur Sa...   \n",
       "15617  578597197782061056  Top story: Jan Böhmermanns Varoufakis-Film ist...   \n",
       "15618  578636536956194816  Jan Böhmermanns Varoufakis-Film ist laut ZDF F...   \n",
       "15619  578696262683357185  Riesen Verwirrung um Varoufakis-Video ZDF-Come...   \n",
       "15620  580328983503466496  #Stinkefinger von Varoufakis: Böhmermann ZDF #...   \n",
       "15621  580330741931909120  #Stinkefinger von Varoufakis: Böhmermann ZDF #...   \n",
       "15622  578297874896805891  ZDF Neo lays into #jauch about the Varoufakis ...   \n",
       "15623  578358505779793920  ZDF Neo : Der Stinkefinger von Varoufakis - Do...   \n",
       "15624  578433150071775232  Un présentateur de la ZDF confesse avoir truqu...   \n",
       "15625  578433646597656576  Oh les kleine menteurs \\\"@CorineBarella: Un pr...   \n",
       "15626  578486910491996160  Este es el programa de ZDF en el que confirman...   \n",
       "15627  578505023912591360  11.34 - wir haben FAST Mittag ▶ Riesen Verwirr...   \n",
       "15628  578305144380612609  Sorry, @yanisvaroufakis! https://t.co/BSkYrbII...   \n",
       "\n",
       "          user_id                                   image_id(s)  \\\n",
       "0       886672620                 boston_fake_03,boston_fake_35   \n",
       "1        21992286                                boston_fake_23   \n",
       "2        16428755                                boston_fake_34   \n",
       "3       303138574                 boston_fake_03,boston_fake_35   \n",
       "4       180460772                                boston_fake_15   \n",
       "5        46224814                                boston_fake_08   \n",
       "6        90735851                                boston_fake_35   \n",
       "7       142785938                 boston_fake_03,boston_fake_35   \n",
       "8        21769179                                boston_fake_13   \n",
       "9        51410043                 boston_fake_03,boston_fake_35   \n",
       "10      224438064                 boston_fake_03,boston_fake_35   \n",
       "11       97034316                 boston_fake_03,boston_fake_35   \n",
       "12       21324077                 boston_fake_03,boston_fake_35   \n",
       "13      201914406                 boston_fake_03,boston_fake_35   \n",
       "14      114819961                 boston_fake_03,boston_fake_35   \n",
       "15       50858723                                boston_fake_16   \n",
       "16      241810080                 boston_fake_03,boston_fake_35   \n",
       "17      109661488  boston_fake_03,boston_fake_07,boston_fake_35   \n",
       "18       16037772                                boston_fake_26   \n",
       "19      396729812                                boston_fake_01   \n",
       "20      266869357                                boston_fake_09   \n",
       "21      363958072                                boston_fake_15   \n",
       "22         608583                 boston_fake_03,boston_fake_35   \n",
       "23      323637124                 boston_fake_03,boston_fake_35   \n",
       "24      105148135                                boston_fake_15   \n",
       "25      419544419                                boston_fake_29   \n",
       "26     1228761494                                boston_fake_15   \n",
       "27      348687483                 boston_fake_03,boston_fake_35   \n",
       "28      394713293                 boston_fake_03,boston_fake_35   \n",
       "29      142869850                                boston_fake_18   \n",
       "...           ...                                           ...   \n",
       "15599  2772819884                                  varoufakis_1   \n",
       "15600   788181799                                  varoufakis_1   \n",
       "15601     9623582                                  varoufakis_1   \n",
       "15602  1304420018                                  varoufakis_1   \n",
       "15603   115913059                                  varoufakis_1   \n",
       "15604   591836976                                  varoufakis_1   \n",
       "15605  1098982633                                  varoufakis_1   \n",
       "15606   592927998                                  varoufakis_1   \n",
       "15607   865194175                                  varoufakis_1   \n",
       "15608   188246428                                  varoufakis_1   \n",
       "15609    27620848                                  varoufakis_1   \n",
       "15610    19026329                                  varoufakis_1   \n",
       "15611   139368676                                  varoufakis_1   \n",
       "15612  1308717630                                  varoufakis_1   \n",
       "15613   580678766                                  varoufakis_1   \n",
       "15614  3070706739                                  varoufakis_1   \n",
       "15615   107723722                                  varoufakis_1   \n",
       "15616   636243442                                  varoufakis_1   \n",
       "15617  1600725278                                  varoufakis_1   \n",
       "15618    44852901                                  varoufakis_1   \n",
       "15619   390148936                                  varoufakis_1   \n",
       "15620  2783698124                                  varoufakis_1   \n",
       "15621  2362990682                                  varoufakis_1   \n",
       "15622    17442320                                  varoufakis_1   \n",
       "15623    19240255                                  varoufakis_1   \n",
       "15624   257551211                                  varoufakis_1   \n",
       "15625    27575883                                  varoufakis_1   \n",
       "15626     2049211                                  varoufakis_1   \n",
       "15627   262222386                                  varoufakis_1   \n",
       "15628    19072286                                  varoufakis_1   \n",
       "\n",
       "              username                       timestamp label  first_image_id  \n",
       "0       SantaCruzShred  Wed Apr 17 18:57:37 +0000 2013  fake  boston_fake_03  \n",
       "1           Oscar_Wang  Fri Apr 19 07:14:23 +0000 2013  fake  boston_fake_23  \n",
       "2               jamwil  Fri Apr 19 07:41:14 +0000 2013  fake  boston_fake_34  \n",
       "3           rubenson80  Wed Apr 17 16:07:12 +0000 2013  fake  boston_fake_03  \n",
       "4            Slimlenny  Wed Apr 17 00:17:06 +0000 2013  fake  boston_fake_15  \n",
       "5         iamyadvinder  Wed Apr 17 17:55:00 +0000 2013  fake  boston_fake_08  \n",
       "6               rcr866  Wed Apr 17 23:27:23 +0000 2013  fake  boston_fake_35  \n",
       "7              GlebGgs  Sat Apr 20 04:21:09 +0000 2013  fake  boston_fake_03  \n",
       "8      larrygloverlive  Fri Apr 19 04:10:19 +0000 2013  fake  boston_fake_13  \n",
       "9         IsraelTucker  Sat Apr 20 20:20:53 +0000 2013  fake  boston_fake_03  \n",
       "10           MrDDS1984  Wed Apr 17 21:36:17 +0000 2013  fake  boston_fake_03  \n",
       "11       AriellMarques  Fri Apr 19 04:18:43 +0000 2013  fake  boston_fake_03  \n",
       "12         SignoreDago  Wed Apr 17 12:23:13 +0000 2013  fake  boston_fake_03  \n",
       "13             mtdisme  Wed Apr 17 19:25:08 +0000 2013  fake  boston_fake_03  \n",
       "14         JaclynIsaac  Wed Apr 17 17:48:31 +0000 2013  fake  boston_fake_03  \n",
       "15            Tadlette  Wed Apr 17 16:21:39 +0000 2013  fake  boston_fake_16  \n",
       "16         FaultyBeard  Wed Apr 17 13:10:11 +0000 2013  fake  boston_fake_03  \n",
       "17           SOS3Today  Wed Apr 17 21:27:35 +0000 2013  fake  boston_fake_03  \n",
       "18             BlakeYo  Wed Apr 17 08:12:35 +0000 2013  fake  boston_fake_26  \n",
       "19         RutgerDrent  Wed Apr 17 15:29:27 +0000 2013  fake  boston_fake_01  \n",
       "20       broadwaybeezy  Tue Apr 16 21:08:09 +0000 2013  fake  boston_fake_09  \n",
       "21     Alpha_and_Delta  Wed Apr 17 13:24:05 +0000 2013  fake  boston_fake_15  \n",
       "22            Zeinobia  Thu Apr 18 20:14:30 +0000 2013  fake  boston_fake_03  \n",
       "23           bruunowtf  Fri Apr 19 01:54:28 +0000 2013  fake  boston_fake_03  \n",
       "24         giggitygreg  Wed Apr 17 01:27:53 +0000 2013  fake  boston_fake_15  \n",
       "25        smitchell550  Wed Apr 17 21:14:30 +0000 2013  fake  boston_fake_29  \n",
       "26      Reddit4Savages  Wed Apr 17 03:00:35 +0000 2013  fake  boston_fake_15  \n",
       "27       isaponferrada  Thu Apr 18 13:17:49 +0000 2013  fake  boston_fake_03  \n",
       "28        AnssiMuhonen  Wed Apr 17 17:06:07 +0000 2013  fake  boston_fake_03  \n",
       "29        brennen_says  Thu Apr 18 17:24:35 +0000 2013  fake  boston_fake_18  \n",
       "...                ...                             ...   ...             ...  \n",
       "15599   denieuwsvolger  Thu Mar 19 11:34:54 +0000 2015  fake    varoufakis_1  \n",
       "15600   nieuws_actueel  Thu Mar 19 11:34:58 +0000 2015  fake    varoufakis_1  \n",
       "15601         jcmraats  Thu Mar 19 11:35:15 +0000 2015  fake    varoufakis_1  \n",
       "15602   Kranten_en_RTV  Thu Mar 19 11:35:33 +0000 2015  fake    varoufakis_1  \n",
       "15603     hardecijfers  Thu Mar 19 11:35:34 +0000 2015  fake    varoufakis_1  \n",
       "15604          vosinfo  Thu Mar 19 11:36:06 +0000 2015  fake    varoufakis_1  \n",
       "15605         wowobbel  Thu Mar 19 11:36:08 +0000 2015  fake    varoufakis_1  \n",
       "15606       Message_11  Thu Mar 19 11:36:20 +0000 2015  fake    varoufakis_1  \n",
       "15607     EricdeBruin4  Thu Mar 19 11:51:25 +0000 2015  fake    varoufakis_1  \n",
       "15608  fn_unterhaltung  Thu Mar 19 12:43:03 +0000 2015  fake    varoufakis_1  \n",
       "15609     freenet_auto  Thu Mar 19 12:43:49 +0000 2015  fake    varoufakis_1  \n",
       "15610        NZ_Online  Thu Mar 19 11:56:18 +0000 2015  fake    varoufakis_1  \n",
       "15611   barkovskymedia  Thu Mar 19 11:58:59 +0000 2015  fake    varoufakis_1  \n",
       "15612  spanjevandaagnl  Thu Mar 19 12:05:11 +0000 2015  fake    varoufakis_1  \n",
       "15613       KORTNEWSNL  Thu Mar 19 12:05:54 +0000 2015  fake    varoufakis_1  \n",
       "15614       Chrisaport  Thu Mar 19 13:41:25 +0000 2015  fake    varoufakis_1  \n",
       "15615   Harald_Bulling  Thu Mar 19 14:15:08 +0000 2015  fake    varoufakis_1  \n",
       "15616   LyingInTheDark  Thu Mar 19 14:36:08 +0000 2015  fake    varoufakis_1  \n",
       "15617  AutomobileGabri  Thu Mar 19 16:41:36 +0000 2015  fake    varoufakis_1  \n",
       "15618    schmitz_bkmks  Thu Mar 19 19:17:55 +0000 2015  fake    varoufakis_1  \n",
       "15619    MatthieuMARIA  Thu Mar 19 23:15:15 +0000 2015  fake    varoufakis_1  \n",
       "15620         TopLinke  Tue Mar 24 11:23:06 +0000 2015  fake    varoufakis_1  \n",
       "15621    DieLinkeKVKNO  Tue Mar 24 11:30:05 +0000 2015  fake    varoufakis_1  \n",
       "15622         jonworth  Wed Mar 18 20:52:12 +0000 2015  fake    varoufakis_1  \n",
       "15623       morgenpost  Thu Mar 19 00:53:07 +0000 2015  fake    varoufakis_1  \n",
       "15624    Cdt_Sylvestre  Thu Mar 19 05:49:44 +0000 2015  fake    varoufakis_1  \n",
       "15625         damomarc  Thu Mar 19 05:51:42 +0000 2015  fake    varoufakis_1  \n",
       "15626    javierpascual  Thu Mar 19 09:23:21 +0000 2015  fake    varoufakis_1  \n",
       "15627      aotto1968_2  Thu Mar 19 10:35:20 +0000 2015  fake    varoufakis_1  \n",
       "15628         janboehm  Wed Mar 18 21:21:05 +0000 2015  fake    varoufakis_1  \n",
       "\n",
       "[15629 rows x 8 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2177, 7)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(15629, 8)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'boston_fake_35',\n",
       " 'eclipse_video_01',\n",
       " 'sandy_real_09',\n",
       " 'sandy_real_10',\n",
       " 'sandy_real_4',\n",
       " 'sandy_real_6',\n",
       " 'sandy_real_90',\n",
       " 'syrianboy_1',\n",
       " 'varoufakis_1'}"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "images_train_dataset = [i for i in train_df['first_image_id'].tolist()]\n",
    "images_train_folder = [i.split('.')[0].strip() for i in listdir('dataset/twitter_final/mediaeval2016/images_train/')]\n",
    "images_train_not_available = set(images_train_dataset)-set(images_train_folder)\n",
    "images_train_not_available"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'airstrikes_1',\n",
       " 'american_soldier_quran_1',\n",
       " 'ankara_explosions_1',\n",
       " 'ankara_explosions_2',\n",
       " 'ankara_explosions_3',\n",
       " 'attacks_paris_16',\n",
       " 'attacks_paris_24',\n",
       " 'attacks_paris_7',\n",
       " 'boko_haram_1',\n",
       " 'brussels_car_metro_1',\n",
       " 'brussels_car_metro_2',\n",
       " 'brussels_car_metro_3',\n",
       " 'brussels_explosions_1',\n",
       " 'brussels_explosions_2',\n",
       " 'brussels_explosions_3',\n",
       " 'convoy_explosion_turkey_1',\n",
       " 'convoy_explosion_turkey_2',\n",
       " 'convoy_explosion_turkey_3',\n",
       " 'donald_trump_attacker_1',\n",
       " 'eagle_kid_1',\n",
       " 'immigrants_1',\n",
       " 'immigrants_2',\n",
       " 'immigrants_3',\n",
       " 'immigrants_4',\n",
       " 'immigrants_5',\n",
       " 'immigrants_6',\n",
       " 'immigrants_7',\n",
       " 'immigrants_8',\n",
       " 'isis_children_1',\n",
       " 'isis_children_2',\n",
       " 'nazi_submarine_2',\n",
       " 'pope_francis_1',\n",
       " 'snowboard_girl_1',\n",
       " 'snowboard_girl_2',\n",
       " 'syrian_children_2'}"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "images_test_dataset = [i.split(',')[0].strip() for i in test_df['image_id'].tolist()]\n",
    "images_test_folder = [i.split('.')[0].strip() for i in listdir('dataset/twitter_final/mediaeval2016/images_test/')]\n",
    "images_test_not_available = set(images_test_dataset)-set(images_test_folder)\n",
    "images_test_not_available"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "images_train_not_available.add('boston_fake_10')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_df = train_df[~train_df['first_image_id'].isin(images_train_not_available)]\n",
    "test_df = test_df[~test_df['image_id'].isin(images_test_not_available)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1040, 7)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(13407, 8)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_text = train_df['post_text'].tolist()\n",
    "test_text = test_df['post_text'].tolist()\n",
    "\n",
    "train_images = [i for i in train_df['first_image_id'].tolist()]\n",
    "test_images = [i.split(',')[0].strip() for i in test_df['image_id'].tolist()]\n",
    "\n",
    "trainY = train_df['label'].tolist()\n",
    "trainY = [1 if i=='real' else 0 for i in trainY]\n",
    "\n",
    "testY = test_df['label'].tolist()\n",
    "testY = [1 if i=='real' else 0 for i in testY]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(13407, 13407, 13407, 1040, 1040, 1040)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(train_text),len(train_images),len(trainY),len(test_text),len(test_images),len(testY)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "# calculate the maximum document length\n",
    "def max_length(lines):\n",
    "    return max([len(s.split()) for s in lines])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "maximum length: 26\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(array([   0.,    3.,  268.,  409.,  581.,  563.,  644.,  602.,  765.,\n",
       "        2583., 2320.,  777., 1717., 1117., 1042.,   16.]),\n",
       " array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 12, 15, 16, 18, 20, 25, 30]),\n",
       " <a list of 16 Patch objects>)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"maximum length:\", max_length(train_text))\n",
    "plt.hist([len(s.split()) for s in train_text],bins=[0,1,2,3,4,5,6,7,8,9,12,15,16,18,20,25,30])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "113.0 13407\n",
      "0.008428432908182293\n"
     ]
    }
   ],
   "source": [
    "l=[len(s.split()) for s in train_text]\n",
    "count=0.0\n",
    "for i in l:\n",
    "    if i>22:\n",
    "        count+=1\n",
    "print(count,len(l))\n",
    "print(count/len(l))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "max_seq_length=23"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "# trainY.count('real'),trainY.count('fake'),testY.count('real'),testY.count('fake')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Text part"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "85d073ba897f499cb55a9c8777f86c98",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(IntProgress(value=0, description='Converting examples to features', max=13407, style=ProgressSt…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "3eb76bb88ee543ea9966a276265e313d",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(IntProgress(value=0, description='Converting examples to features', max=1040, style=ProgressSty…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "class PaddingInputExample(object):\n",
    "    \"\"\"Fake example so the num input examples is a multiple of the batch size.\n",
    "  When running eval/predict on the TPU, we need to pad the number of examples\n",
    "  to be a multiple of the batch size, because the TPU requires a fixed batch\n",
    "  size. The alternative is to drop the last batch, which is bad because it means\n",
    "  the entire output data won't be generated.\n",
    "  We use this class instead of `None` because treating `None` as padding\n",
    "  battches could cause silent errors.\n",
    "  \"\"\"\n",
    "\n",
    "class InputExample(object):\n",
    "    \"\"\"A single training/test example for simple sequence classification.\"\"\"\n",
    "\n",
    "    def __init__(self, guid, text_a, text_b=None, label=None):\n",
    "        \"\"\"Constructs a InputExample.\n",
    "    Args:\n",
    "      guid: Unique id for the example.\n",
    "      text_a: string. The untokenized text of the first sequence. For single\n",
    "        sequence tasks, only this sequence must be specified.\n",
    "      text_b: (Optional) string. The untokenized text of the second sequence.\n",
    "        Only must be specified for sequence pair tasks.\n",
    "      label: (Optional) string. The label of the example. This should be\n",
    "        specified for train and dev examples, but not for test examples.\n",
    "    \"\"\"\n",
    "        self.guid = guid\n",
    "        self.text_a = text_a\n",
    "        self.text_b = text_b\n",
    "        self.label = label\n",
    "\n",
    "def create_tokenizer_from_hub_module():\n",
    "    \"\"\"Get the vocab file and casing info from the Hub module.\"\"\"\n",
    "    bert_module =  hub.Module(bert_path)\n",
    "    tokenization_info = bert_module(signature=\"tokenization_info\", as_dict=True)\n",
    "    vocab_file, do_lower_case = sess.run(\n",
    "        [\n",
    "            tokenization_info[\"vocab_file\"],\n",
    "            tokenization_info[\"do_lower_case\"],\n",
    "        ]\n",
    "    )\n",
    "\n",
    "    return FullTokenizer(vocab_file=vocab_file, do_lower_case=do_lower_case)\n",
    "\n",
    "def convert_single_example(tokenizer, example, max_seq_length=256):\n",
    "    \"\"\"Converts a single `InputExample` into a single `InputFeatures`.\"\"\"\n",
    "\n",
    "    if isinstance(example, PaddingInputExample):\n",
    "        input_ids = [0] * max_seq_length\n",
    "        input_mask = [0] * max_seq_length\n",
    "        segment_ids = [0] * max_seq_length\n",
    "        label = 0\n",
    "        return input_ids, input_mask, segment_ids, label\n",
    "\n",
    "    tokens_a = tokenizer.tokenize(example.text_a)\n",
    "    if len(tokens_a) > max_seq_length - 2:\n",
    "        tokens_a = tokens_a[0 : (max_seq_length - 2)]\n",
    "\n",
    "    tokens = []\n",
    "    segment_ids = []\n",
    "    tokens.append(\"[CLS]\")\n",
    "    segment_ids.append(0)\n",
    "    for token in tokens_a:\n",
    "        tokens.append(token)\n",
    "        segment_ids.append(0)\n",
    "    tokens.append(\"[SEP]\")\n",
    "    segment_ids.append(0)\n",
    "\n",
    "    input_ids = tokenizer.convert_tokens_to_ids(tokens)\n",
    "\n",
    "    # The mask has 1 for real tokens and 0 for padding tokens. Only real\n",
    "    # tokens are attended to.\n",
    "    input_mask = [1] * len(input_ids)\n",
    "\n",
    "    # Zero-pad up to the sequence length.\n",
    "    while len(input_ids) < max_seq_length:\n",
    "        input_ids.append(0)\n",
    "        input_mask.append(0)\n",
    "        segment_ids.append(0)\n",
    "\n",
    "    assert len(input_ids) == max_seq_length\n",
    "    assert len(input_mask) == max_seq_length\n",
    "    assert len(segment_ids) == max_seq_length\n",
    "\n",
    "    return input_ids, input_mask, segment_ids, example.label\n",
    "\n",
    "def convert_examples_to_features(tokenizer, examples, max_seq_length=256):\n",
    "    \"\"\"Convert a set of `InputExample`s to a list of `InputFeatures`.\"\"\"\n",
    "\n",
    "    input_ids, input_masks, segment_ids, labels = [], [], [], []\n",
    "    for example in tqdm_notebook(examples, desc=\"Converting examples to features\"):\n",
    "        input_id, input_mask, segment_id, label = convert_single_example(\n",
    "            tokenizer, example, max_seq_length\n",
    "        )\n",
    "        input_ids.append(input_id)\n",
    "        input_masks.append(input_mask)\n",
    "        segment_ids.append(segment_id)\n",
    "        labels.append(label)\n",
    "    return (\n",
    "        np.array(input_ids),\n",
    "        np.array(input_masks),\n",
    "        np.array(segment_ids),\n",
    "        np.array(labels).reshape(-1, 1),\n",
    "    )\n",
    "\n",
    "def convert_text_to_examples(texts, labels):\n",
    "    \"\"\"Create InputExamples\"\"\"\n",
    "    InputExamples = []\n",
    "    for text, label in zip(texts, labels):\n",
    "        InputExamples.append(\n",
    "            InputExample(guid=None, text_a=\" \".join(text), text_b=None, label=label)\n",
    "        )\n",
    "    return InputExamples\n",
    "\n",
    "# Instantiate tokenizer\n",
    "tokenizer = create_tokenizer_from_hub_module()\n",
    "\n",
    "# Convert data to InputExample format\n",
    "train_examples = convert_text_to_examples(train_text, trainY)\n",
    "test_examples = convert_text_to_examples(test_text, testY)\n",
    "\n",
    "# Convert to features\n",
    "(train_input_ids, train_input_masks, train_segment_ids, trainY \n",
    ") = convert_examples_to_features(tokenizer, train_examples, max_seq_length=max_seq_length)\n",
    "(test_input_ids, test_input_masks, test_segment_ids, testY\n",
    ") = convert_examples_to_features(tokenizer, test_examples, max_seq_length=max_seq_length)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Image Part"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "length = 224\n",
    "width = 224\n",
    "channels = 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "def read_and_process_image(list_of_images):\n",
    "    X = [] \n",
    "    for image in tqdm(list_of_images):\n",
    "#         print(image)\n",
    "        X.append(cv2.resize(cv2.imread(image, cv2.IMREAD_COLOR), (length,width), interpolation=cv2.INTER_CUBIC))  \n",
    "    return X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "images = listdir('dataset/twitter_final/mediaeval2016/images_train/')\n",
    "images.extend(listdir('dataset/twitter_final/mediaeval2016/images_test/'))\n",
    "jpg = []\n",
    "png=[]\n",
    "jpeg=[]\n",
    "gif = []\n",
    "\n",
    "for i in images:\n",
    "    name,ext = i.split('.')[0],i.split('.')[-1]\n",
    "    eval(ext).append(name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_extension_of_file(file_name):\n",
    "    if file_name in jpg:\n",
    "        return '.jpg'\n",
    "    elif file_name in png:\n",
    "        return '.png'\n",
    "    elif file_name in jpeg:\n",
    "        return '.jpeg'\n",
    "    else:\n",
    "        return '.gif'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_images = ['dataset/twitter_final/mediaeval2016/images_train/'+i+get_extension_of_file(i) for i in train_images]\n",
    "test_images = ['dataset/twitter_final/mediaeval2016/images_test/'+i+get_extension_of_file(i) for i in test_images]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "# train_imagesX = read_and_process_image(train_images)\n",
    "# test_imagesX = read_and_process_image(test_images)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "# np.save('train_imagesX.npy', train_imagesX)\n",
    "# np.save('test_imagesX.npy', test_imagesX)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_imagesX = np.load('train_imagesX.npy')\n",
    "test_imagesX = np.load('test_imagesX.npy')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_imagesX = np.rollaxis(train_imagesX, 3, 1)\n",
    "test_imagesX = np.rollaxis(test_imagesX,3,1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# The Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "class BertLayer(tf.layers.Layer):\n",
    "    def __init__(self, n_fine_tune_layers=10, **kwargs):\n",
    "        self.n_fine_tune_layers = n_fine_tune_layers\n",
    "        self.trainable = True\n",
    "        self.output_size = 768\n",
    "        super(BertLayer, self).__init__(**kwargs)\n",
    "\n",
    "    def build(self, input_shape):\n",
    "        self.bert = hub.Module(\n",
    "            bert_path,\n",
    "            trainable=self.trainable,\n",
    "            name=\"{}_module\".format(self.name)\n",
    "        )\n",
    "\n",
    "        trainable_vars = self.bert.variables\n",
    "\n",
    "        # Remove unused layers\n",
    "        trainable_vars = [var for var in trainable_vars if not \"/cls/\" in var.name]\n",
    "\n",
    "        # Select how many layers to fine tune\n",
    "        trainable_vars = trainable_vars[-self.n_fine_tune_layers :]\n",
    "\n",
    "        # Add to trainable weights\n",
    "        for var in trainable_vars:\n",
    "            self._trainable_weights.append(var)\n",
    "            \n",
    "        for var in self.bert.variables:\n",
    "            if var not in self._trainable_weights:\n",
    "                self._non_trainable_weights.append(var)\n",
    "\n",
    "        super(BertLayer, self).build(input_shape)\n",
    "\n",
    "    def call(self, inputs):\n",
    "        inputs = [K.cast(x, dtype=\"int32\") for x in inputs]\n",
    "        input_ids, input_mask, segment_ids = inputs\n",
    "        bert_inputs = dict(\n",
    "            input_ids=input_ids, input_mask=input_mask, segment_ids=segment_ids\n",
    "        )\n",
    "        result = self.bert(inputs=bert_inputs, signature=\"tokens\", as_dict=True)[\n",
    "            \"pooled_output\"\n",
    "        ]\n",
    "        return result\n",
    "\n",
    "    def compute_output_shape(self, input_shape):\n",
    "        return (input_shape[0], self.output_size)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "def initialize_vars(sess):\n",
    "    sess = tf.Session(config=tf.ConfigProto(allow_soft_placement=True, log_device_placement=True)) #command to run codeon multiple gpu\n",
    "    sess.run(tf.local_variables_initializer())\n",
    "    sess.run(tf.global_variables_initializer())\n",
    "    sess.run(tf.tables_initializer())\n",
    "    K.set_session(sess)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "# base = tf.keras.applications.VGG19(weights='imagenet', include_top=False, input_shape=(3,224,224))\n",
    "# base.trainable=False\n",
    "first = True"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "metadata": {},
   "outputs": [],
   "source": [
    "#text model\n",
    "def news_model(x_train, y_train, x_val, y_val, params):\n",
    "    \n",
    "#     pprint(params)\n",
    "    try:\n",
    "        del model\n",
    "    except:\n",
    "        pass\n",
    "    K.clear_session()\n",
    "    gc.collect()\n",
    "    \n",
    "    with tf.device('/cpu:0'):\n",
    "        bert_base = BertLayer()\n",
    "        bert_base.trainable= params['bert_trainable']\n",
    "\n",
    "        in_id = tf.keras.layers.Input(shape=(max_seq_length,), name=\"input_ids\")\n",
    "        in_mask = tf.keras.layers.Input(shape=(max_seq_length,), name=\"input_masks\")\n",
    "        in_segment = tf.keras.layers.Input(shape=(max_seq_length,), name=\"segment_ids\")\n",
    "        bert_inputs = [in_id, in_mask, in_segment]\n",
    "        bert_output = bert_base(bert_inputs)\n",
    "\n",
    "        if params['text_no_hidden_layer']>0:\n",
    "            for i in range(params['text_no_hidden_layer']):\n",
    "                bert_output = tf.keras.layers.Dense(params['text_hidden_neurons'], activation='relu')(bert_output)\n",
    "                bert_output = tf.keras.layers.Dropout(params['dropout'])(bert_output)\n",
    "\n",
    "        text_repr = tf.keras.layers.Dense(params['repr_size'], activation='relu')(bert_output)\n",
    "\n",
    "        #image model\n",
    "        conv_base = tf.keras.applications.VGG19(weights='imagenet', include_top=False, input_shape=(3,224,224))\n",
    "        conv_base.trainable=False\n",
    "#         conv_base = base\n",
    "\n",
    "        input_image = tf.keras.layers.Input(shape=(3,224,224))\n",
    "        base_output = conv_base(input_image)\n",
    "        flat = tf.keras.layers.Flatten()(base_output)\n",
    "\n",
    "        if params['vis_no_hidden_layer']>0:\n",
    "            for i in range(params['vis_no_hidden_layer']):\n",
    "                flat = tf.keras.layers.Dense(params['vis_hidden_neurons'], activation='relu')(flat)\n",
    "                flat = tf.keras.layers.Dropout(params['dropout'])(flat)\n",
    "\n",
    "        visual_repr = tf.keras.layers.Dense(params['repr_size'],activation='relu')(flat)\n",
    "\n",
    "\n",
    "        #classifier\n",
    "        combine_repr = tf.keras.layers.concatenate([text_repr, visual_repr])\n",
    "        com_drop=tf.keras.layers.Dropout(params['dropout'])(combine_repr)\n",
    "\n",
    "        if params['final_no_hidden_layer']>0:\n",
    "            for i in range(params['final_no_hidden_layer']):\n",
    "                com_drop = tf.keras.layers.Dense(params['final_hidden_neurons'], activation='relu')(com_drop)\n",
    "                com_drop=tf.keras.layers.Dropout(params['dropout'])(com_drop)\n",
    "\n",
    "        prediction = tf.keras.layers.Dense(1,activation='sigmoid')(com_drop)\n",
    "\n",
    "        model = tf.keras.models.Model(inputs=[in_id,in_mask,in_segment,input_image], outputs=prediction)\n",
    "\n",
    "    model = tf.keras.utils.multi_gpu_model(model,gpus=4)\n",
    "    \n",
    "#     if params['optimizer'] == 'adam':\n",
    "#         opt = tf.keras.optimizers.Adam(lr=0.0005)\n",
    "#     else:\n",
    "#         opt = tf.keras.optimizers.RMSprop(lr=0.00005)\n",
    "        \n",
    "#     model.compile(loss='binary_crossentropy', optimizer=params['optimizer'](lr=lr_normalizer(params['lr'], params['optimizer'])), metrics=['accuracy'])\n",
    "    model.compile(loss='binary_crossentropy', optimizer=params['optimizer'](lr=params['lr']), metrics=['accuracy'])\n",
    "    initialize_vars(sess)\n",
    "    \n",
    "    out = model.fit(x_train, y_train,\n",
    "                    batch_size=params['batch_size'],\n",
    "                    epochs=params['epochs'],\n",
    "                    verbose=0,\n",
    "                    shuffle=True,\n",
    "                    validation_data=[x_val, y_val],callbacks=[live()])\n",
    "    \n",
    "    return out, model\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "params = {\n",
    "    'bert_trainable' :[False,True],\n",
    "    'text_no_hidden_layer':(0,3,3),\n",
    "    'text_hidden_neurons':[768,400,32],\n",
    "    'dropout':(0.3,0.7,4),\n",
    "    'repr_size':[32],\n",
    "    'vis_no_hidden_layer':(0,3,3),\n",
    "    'vis_hidden_neurons':[4096,2742,1388,32],\n",
    "    'final_no_hidden_layer':(0,3,3),\n",
    "    'final_hidden_neurons':[64,35,5],\n",
    "    'optimizer':['adam','rmsprop'],\n",
    "    'batch_size':[512],\n",
    "    'epochs':[10]\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 31/31 [3:36:11<00:00, 409.80s/it]\n"
     ]
    }
   ],
   "source": [
    "h = ta.Scan([train_input_ids, train_input_masks, train_segment_ids,train_imagesX], trainY, params=params,\n",
    "            model=news_model,\n",
    "            dataset_name='twitter_fake_news',\n",
    "            experiment_no='1',\n",
    "            x_val=[test_input_ids, test_input_masks, test_segment_ids,test_imagesX],\n",
    "            y_val=testY,\n",
    "            grid_downsample=.002)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Lets look at Hyper parameter experiment 1 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "r = ta.Reporting(h)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7230769230769231"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r.high('val_acc')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 756x756 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "r.plot_corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "bert_trainable\n",
      "text_no_hidden_layer\n",
      "text_hidden_neurons\n",
      "dropout\n",
      "repr_size\n",
      "vis_no_hidden_layer\n",
      "vis_hidden_neurons\n",
      "final_no_hidden_layer\n",
      "final_hidden_neurons\n",
      "optimizer\n",
      "batch_size\n",
      "epochs\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x475.2 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x475.2 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x475.2 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x475.2 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x475.2 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x475.2 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x475.2 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x475.2 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x475.2 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x475.2 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x475.2 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x475.2 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for k in params.keys():\n",
    "    print(k)\n",
    "    r.plot_box(y='val_acc',x=k)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fecd5307f98>"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.preprocessing import MinMaxScaler\n",
    "\n",
    "X = r.data[['bert_trainable','text_no_hidden_layer','text_hidden_neurons','dropout','vis_no_hidden_layer','vis_hidden_neurons','final_no_hidden_layer','final_hidden_neurons','optimizer']]\n",
    "scaler = MinMaxScaler()\n",
    "y = scaler.fit_transform(r.data[['val_acc']])\n",
    "X['bert_trainable'] = pd.factorize(X['bert_trainable'])[0]\n",
    "X['optimizer'] = pd.factorize(X['optimizer'])[0]\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "\n",
    "reg = RandomForestRegressor(max_depth=3,n_estimators=100)\n",
    "reg.fit(X,y)\n",
    "pd.Series(reg.feature_importances_,index=X.columns).\\\n",
    "sort_values(ascending=True).plot.barh(color='grey',title='Feature Importance of Hyperparameters')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x475.2 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "r.plot_regs(x='acc',y='val_acc')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1344.48x288 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "r.plot_bars(x='vis_no_hidden_layer',y='val_acc',hue='dropout',col='final_no_hidden_layer')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x475.2 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "r.plot_kde()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Hyperparameter experiment no 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [],
   "source": [
    "params = {\n",
    "    'bert_trainable' :[False],\n",
    "    'text_no_hidden_layer':[0,1],\n",
    "    'text_hidden_neurons':[768],\n",
    "    'dropout':[0.4],\n",
    "    'repr_size':[32],\n",
    "    'vis_no_hidden_layer':[1],\n",
    "    'vis_hidden_neurons':[2742],\n",
    "    'final_no_hidden_layer':[0,1],\n",
    "    'final_hidden_neurons':[35],\n",
    "    'optimizer':[tf.keras.optimizers.Adam,tf.keras.optimizers.RMSprop],\n",
    "    'batch_size':[512],\n",
    "    'epochs':[10],\n",
    "    'lr':(0.0001,0.1,10)\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "100%|██████████| 40/40 [4:34:48<00:00, 419.05s/it]\u001b[A"
     ]
    }
   ],
   "source": [
    "h = ta.Scan([train_input_ids, train_input_masks, train_segment_ids,train_imagesX], trainY, params=params,\n",
    "            model=news_model,\n",
    "            dataset_name='twitter_fake_news',\n",
    "            experiment_no='2',\n",
    "            x_val=[test_input_ids, test_input_masks, test_segment_ids,test_imagesX],\n",
    "            y_val=testY,\n",
    "            grid_downsample=.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [],
   "source": [
    "r = ta.Reporting(h)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7346153846153847"
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r.high('val_acc')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>round_epochs</th>\n",
       "      <th>val_loss</th>\n",
       "      <th>val_acc</th>\n",
       "      <th>loss</th>\n",
       "      <th>acc</th>\n",
       "      <th>bert_trainable</th>\n",
       "      <th>text_no_hidden_layer</th>\n",
       "      <th>text_hidden_neurons</th>\n",
       "      <th>dropout</th>\n",
       "      <th>repr_size</th>\n",
       "      <th>vis_no_hidden_layer</th>\n",
       "      <th>vis_hidden_neurons</th>\n",
       "      <th>final_no_hidden_layer</th>\n",
       "      <th>final_hidden_neurons</th>\n",
       "      <th>optimizer</th>\n",
       "      <th>batch_size</th>\n",
       "      <th>epochs</th>\n",
       "      <th>lr</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>10</td>\n",
       "      <td>0.680248</td>\n",
       "      <td>0.676923</td>\n",
       "      <td>0.128123</td>\n",
       "      <td>0.959275</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>768</td>\n",
       "      <td>0.4</td>\n",
       "      <td>32</td>\n",
       "      <td>1</td>\n",
       "      <td>2742</td>\n",
       "      <td>0</td>\n",
       "      <td>35</td>\n",
       "      <td>&lt;class 'tensorflow.python.keras.optimizers.Adam'&gt;</td>\n",
       "      <td>512</td>\n",
       "      <td>10</td>\n",
       "      <td>0.00010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>10</td>\n",
       "      <td>5.140785</td>\n",
       "      <td>0.679808</td>\n",
       "      <td>7.354664</td>\n",
       "      <td>0.542254</td>\n",
       "      <td>False</td>\n",
       "      <td>1</td>\n",
       "      <td>768</td>\n",
       "      <td>0.4</td>\n",
       "      <td>32</td>\n",
       "      <td>1</td>\n",
       "      <td>2742</td>\n",
       "      <td>1</td>\n",
       "      <td>35</td>\n",
       "      <td>&lt;class 'tensorflow.python.keras.optimizers.Adam'&gt;</td>\n",
       "      <td>512</td>\n",
       "      <td>10</td>\n",
       "      <td>0.07003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>10</td>\n",
       "      <td>0.656070</td>\n",
       "      <td>0.696154</td>\n",
       "      <td>0.154333</td>\n",
       "      <td>0.950324</td>\n",
       "      <td>False</td>\n",
       "      <td>1</td>\n",
       "      <td>768</td>\n",
       "      <td>0.4</td>\n",
       "      <td>32</td>\n",
       "      <td>1</td>\n",
       "      <td>2742</td>\n",
       "      <td>1</td>\n",
       "      <td>35</td>\n",
       "      <td>&lt;class 'tensorflow.python.keras.optimizers.Adam'&gt;</td>\n",
       "      <td>512</td>\n",
       "      <td>10</td>\n",
       "      <td>0.00010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>10</td>\n",
       "      <td>0.675741</td>\n",
       "      <td>0.700962</td>\n",
       "      <td>0.331237</td>\n",
       "      <td>0.851123</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>768</td>\n",
       "      <td>0.4</td>\n",
       "      <td>32</td>\n",
       "      <td>1</td>\n",
       "      <td>2742</td>\n",
       "      <td>1</td>\n",
       "      <td>35</td>\n",
       "      <td>&lt;class 'tensorflow.python.keras.optimizers.RMS...</td>\n",
       "      <td>512</td>\n",
       "      <td>10</td>\n",
       "      <td>0.00010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>10</td>\n",
       "      <td>0.670797</td>\n",
       "      <td>0.734615</td>\n",
       "      <td>0.283395</td>\n",
       "      <td>0.881927</td>\n",
       "      <td>False</td>\n",
       "      <td>1</td>\n",
       "      <td>768</td>\n",
       "      <td>0.4</td>\n",
       "      <td>32</td>\n",
       "      <td>1</td>\n",
       "      <td>2742</td>\n",
       "      <td>0</td>\n",
       "      <td>35</td>\n",
       "      <td>&lt;class 'tensorflow.python.keras.optimizers.RMS...</td>\n",
       "      <td>512</td>\n",
       "      <td>10</td>\n",
       "      <td>0.00010</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    round_epochs  val_loss   val_acc      loss       acc bert_trainable  \\\n",
       "5             10  0.680248  0.676923  0.128123  0.959275          False   \n",
       "34            10  5.140785  0.679808  7.354664  0.542254          False   \n",
       "23            10  0.656070  0.696154  0.154333  0.950324          False   \n",
       "25            10  0.675741  0.700962  0.331237  0.851123          False   \n",
       "26            10  0.670797  0.734615  0.283395  0.881927          False   \n",
       "\n",
       "    text_no_hidden_layer  text_hidden_neurons  dropout  repr_size  \\\n",
       "5                      0                  768      0.4         32   \n",
       "34                     1                  768      0.4         32   \n",
       "23                     1                  768      0.4         32   \n",
       "25                     0                  768      0.4         32   \n",
       "26                     1                  768      0.4         32   \n",
       "\n",
       "    vis_no_hidden_layer  vis_hidden_neurons  final_no_hidden_layer  \\\n",
       "5                     1                2742                      0   \n",
       "34                    1                2742                      1   \n",
       "23                    1                2742                      1   \n",
       "25                    1                2742                      1   \n",
       "26                    1                2742                      0   \n",
       "\n",
       "    final_hidden_neurons                                          optimizer  \\\n",
       "5                     35  <class 'tensorflow.python.keras.optimizers.Adam'>   \n",
       "34                    35  <class 'tensorflow.python.keras.optimizers.Adam'>   \n",
       "23                    35  <class 'tensorflow.python.keras.optimizers.Adam'>   \n",
       "25                    35  <class 'tensorflow.python.keras.optimizers.RMS...   \n",
       "26                    35  <class 'tensorflow.python.keras.optimizers.RMS...   \n",
       "\n",
       "    batch_size  epochs       lr  \n",
       "5          512      10  0.00010  \n",
       "34         512      10  0.07003  \n",
       "23         512      10  0.00010  \n",
       "25         512      10  0.00010  \n",
       "26         512      10  0.00010  "
      ]
     },
     "execution_count": 140,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r.data.sort_values('val_acc').tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 792x792 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "r.plot_corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "bert_trainable\n",
      "text_no_hidden_layer\n",
      "text_hidden_neurons\n",
      "dropout\n",
      "repr_size\n",
      "vis_no_hidden_layer\n",
      "vis_hidden_neurons\n",
      "final_no_hidden_layer\n",
      "final_hidden_neurons\n",
      "optimizer\n",
      "batch_size\n",
      "epochs\n",
      "lr\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x475.2 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x475.2 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x475.2 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x475.2 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x475.2 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x475.2 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x475.2 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x475.2 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x475.2 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x475.2 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x475.2 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x475.2 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x475.2 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for k in params.keys():\n",
    "    print(k)\n",
    "    r.plot_box(y='val_acc',x=k)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fecc6ec5208>"
      ]
     },
     "execution_count": 142,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.preprocessing import MinMaxScaler\n",
    "\n",
    "X = r.data[['text_no_hidden_layer','final_no_hidden_layer','optimizer','lr']]\n",
    "scaler = MinMaxScaler()\n",
    "y = scaler.fit_transform(r.data[['val_acc']])\n",
    "X['optimizer'] = pd.factorize(X['optimizer'])[0]\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "\n",
    "reg = RandomForestRegressor(max_depth=3,n_estimators=100)\n",
    "reg.fit(X,y)\n",
    "pd.Series(reg.feature_importances_,index=X.columns).\\\n",
    "sort_values(ascending=True).plot.barh(color='grey',title='Feature Importance of Hyperparameters')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x475.2 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "r.plot_regs(x='acc',y='val_acc')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x475.2 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "r.plot_kde()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Wring Lr range led to bad results....repeating with confind lr values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using LR finder to find best learning rate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {},
   "outputs": [],
   "source": [
    "#text model\n",
    "def get_news_model( params):\n",
    "    \n",
    "#     pprint(params)\n",
    "    K.clear_session()\n",
    "    \n",
    "    with tf.device('/cpu:0'):\n",
    "        bert_base = BertLayer()\n",
    "        bert_base.trainable= params['bert_trainable']\n",
    "\n",
    "        in_id = tf.keras.layers.Input(shape=(max_seq_length,), name=\"input_ids\")\n",
    "        in_mask = tf.keras.layers.Input(shape=(max_seq_length,), name=\"input_masks\")\n",
    "        in_segment = tf.keras.layers.Input(shape=(max_seq_length,), name=\"segment_ids\")\n",
    "        bert_inputs = [in_id, in_mask, in_segment]\n",
    "        bert_output = bert_base(bert_inputs)\n",
    "\n",
    "        if params['text_no_hidden_layer']>0:\n",
    "            for i in range(params['text_no_hidden_layer']):\n",
    "                bert_output = tf.keras.layers.Dense(params['text_hidden_neurons'], activation='relu')(bert_output)\n",
    "                bert_output = tf.keras.layers.Dropout(params['dropout'])(bert_output)\n",
    "\n",
    "        text_repr = tf.keras.layers.Dense(params['repr_size'], activation='relu')(bert_output)\n",
    "\n",
    "        #image model\n",
    "        conv_base = tf.keras.applications.VGG19(weights='imagenet', include_top=False, input_shape=(3,224,224))\n",
    "        conv_base.trainable=False\n",
    "#         conv_base = base\n",
    "\n",
    "        input_image = tf.keras.layers.Input(shape=(3,224,224))\n",
    "        base_output = conv_base(input_image)\n",
    "        flat = tf.keras.layers.Flatten()(base_output)\n",
    "\n",
    "        if params['vis_no_hidden_layer']>0:\n",
    "            for i in range(params['vis_no_hidden_layer']):\n",
    "                flat = tf.keras.layers.Dense(params['vis_hidden_neurons'], activation='relu')(flat)\n",
    "                flat = tf.keras.layers.Dropout(params['dropout'])(flat)\n",
    "\n",
    "        visual_repr = tf.keras.layers.Dense(params['repr_size'],activation='relu')(flat)\n",
    "\n",
    "\n",
    "        #classifier\n",
    "        combine_repr = tf.keras.layers.concatenate([text_repr, visual_repr])\n",
    "        com_drop=tf.keras.layers.Dropout(params['dropout'])(combine_repr)\n",
    "\n",
    "        if params['final_no_hidden_layer']>0:\n",
    "            for i in range(params['final_no_hidden_layer']):\n",
    "                com_drop = tf.keras.layers.Dense(params['final_hidden_neurons'], activation='relu')(com_drop)\n",
    "                com_drop=tf.keras.layers.Dropout(params['dropout'])(com_drop)\n",
    "\n",
    "        prediction = tf.keras.layers.Dense(1,activation='sigmoid')(com_drop)\n",
    "\n",
    "        model = tf.keras.models.Model(inputs=[in_id,in_mask,in_segment,input_image], outputs=prediction)\n",
    "\n",
    "    model = tf.keras.utils.multi_gpu_model(model,gpus=4)\n",
    "    \n",
    "#     if params['optimizer'] == 'adam':\n",
    "#         opt = tf.keras.optimizers.Adam(lr=0.0005)\n",
    "#     else:\n",
    "#         opt = tf.keras.optimizers.RMSprop(lr=0.00005)\n",
    "        \n",
    "    model.compile(loss='binary_crossentropy', optimizer=params['optimizer'](), metrics=['accuracy'])\n",
    "    initialize_vars(sess)\n",
    "    \n",
    "    \n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {},
   "outputs": [],
   "source": [
    "params_adam = {\n",
    "    'bert_trainable' :False,\n",
    "    'text_no_hidden_layer':1,\n",
    "    'text_hidden_neurons':768,\n",
    "    'dropout':0.4,\n",
    "    'repr_size':32,\n",
    "    'vis_no_hidden_layer':1,\n",
    "    'vis_hidden_neurons':2742,\n",
    "    'final_no_hidden_layer':1,\n",
    "    'final_hidden_neurons':35,\n",
    "    'optimizer':tf.keras.optimizers.Adam\n",
    "}\n",
    "\n",
    "params_rms = {\n",
    "    'bert_trainable' :False,\n",
    "    'text_no_hidden_layer':1,\n",
    "    'text_hidden_neurons':768,\n",
    "    'dropout':0.4,\n",
    "    'repr_size':32,\n",
    "    'vis_no_hidden_layer':1,\n",
    "    'vis_hidden_neurons':2742,\n",
    "    'final_no_hidden_layer':1,\n",
    "    'final_hidden_neurons':35,\n",
    "    'optimizer':tf.keras.optimizers.RMSprop\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {},
   "outputs": [],
   "source": [
    "model_adam=get_news_model(params_adam)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {},
   "outputs": [],
   "source": [
    "lr_finder = LRFinder(model_adam)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/5\n",
      "13407/13407 [==============================] - 35s 3ms/step - loss: 0.7360 - acc: 0.5073\n",
      "Epoch 2/5\n",
      "13407/13407 [==============================] - 27s 2ms/step - loss: 0.6535 - acc: 0.6042\n",
      "Epoch 3/5\n",
      "13407/13407 [==============================] - 27s 2ms/step - loss: 0.4836 - acc: 0.7735\n",
      "Epoch 4/5\n",
      "13407/13407 [==============================] - 26s 2ms/step - loss: 0.4288 - acc: 0.8142\n",
      "Epoch 5/5\n",
      " 3072/13407 [=====>........................] - ETA: 20s - loss: 0.5651 - acc: 0.8122"
     ]
    }
   ],
   "source": [
    "lr_finder.find([train_input_ids, train_input_masks, train_segment_ids,train_imagesX], trainY, 0.000001, 0.01, 512, 5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "lr_finder.plot_loss()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {},
   "outputs": [],
   "source": [
    "model_rms=get_news_model(params_rms)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {},
   "outputs": [],
   "source": [
    "lr_finder = LRFinder(model_rms)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/5\n",
      "13407/13407 [==============================] - 36s 3ms/step - loss: 0.7812 - acc: 0.4868\n",
      "Epoch 2/5\n",
      "13407/13407 [==============================] - 28s 2ms/step - loss: 0.6864 - acc: 0.5785\n",
      "Epoch 3/5\n",
      "13407/13407 [==============================] - 27s 2ms/step - loss: 0.6805 - acc: 0.6040\n",
      "Epoch 4/5\n",
      "13407/13407 [==============================] - 28s 2ms/step - loss: 0.7247 - acc: 0.5587\n",
      "Epoch 5/5\n",
      "13407/13407 [==============================] - 28s 2ms/step - loss: 0.8151 - acc: 0.5264\n"
     ]
    }
   ],
   "source": [
    "lr_finder.find([train_input_ids, train_input_masks, train_segment_ids,train_imagesX], trainY, 0.000001, 0.01, 512, 5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "lr_finder.plot_loss()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "RE-running LR finder on narrow range"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "metadata": {},
   "outputs": [],
   "source": [
    "model_adam=get_news_model(params_adam)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "metadata": {},
   "outputs": [],
   "source": [
    "lr_finder = LRFinder(model_adam)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/7\n",
      "13407/13407 [==============================] - 37s 3ms/step - loss: 0.6677 - acc: 0.5845\n",
      "Epoch 2/7\n",
      "13407/13407 [==============================] - 27s 2ms/step - loss: 0.5492 - acc: 0.6987\n",
      "Epoch 3/7\n",
      "13407/13407 [==============================] - 27s 2ms/step - loss: 0.4803 - acc: 0.7498\n",
      "Epoch 4/7\n",
      "13407/13407 [==============================] - 27s 2ms/step - loss: 0.3799 - acc: 0.8197\n",
      "Epoch 5/7\n",
      "13407/13407 [==============================] - 27s 2ms/step - loss: 0.3127 - acc: 0.8684\n",
      "Epoch 6/7\n",
      "13407/13407 [==============================] - 28s 2ms/step - loss: 0.2553 - acc: 0.8971\n",
      "Epoch 7/7\n",
      "13407/13407 [==============================] - 28s 2ms/step - loss: 0.2565 - acc: 0.8999\n"
     ]
    }
   ],
   "source": [
    "lr_finder.find([train_input_ids, train_input_masks, train_segment_ids,train_imagesX], trainY, 0.00005, 0.001, 512, 7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "lr_finder.plot_loss()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "metadata": {},
   "outputs": [],
   "source": [
    "model_rms=get_news_model(params_rms)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "metadata": {},
   "outputs": [],
   "source": [
    "lr_finder = LRFinder(model_rms)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/8\n",
      "13407/13407 [==============================] - 37s 3ms/step - loss: 0.6680 - acc: 0.5911\n",
      "Epoch 2/8\n",
      "13407/13407 [==============================] - 28s 2ms/step - loss: 0.5903 - acc: 0.6925\n",
      "Epoch 3/8\n",
      "13407/13407 [==============================] - 28s 2ms/step - loss: 0.5627 - acc: 0.7102\n",
      "Epoch 4/8\n",
      "13407/13407 [==============================] - 28s 2ms/step - loss: 0.5593 - acc: 0.7160\n",
      "Epoch 5/8\n",
      "13407/13407 [==============================] - 28s 2ms/step - loss: 0.5626 - acc: 0.7093\n",
      "Epoch 6/8\n",
      "13407/13407 [==============================] - 28s 2ms/step - loss: 0.5798 - acc: 0.6865\n",
      "Epoch 7/8\n",
      "13407/13407 [==============================] - 28s 2ms/step - loss: 0.6296 - acc: 0.6698\n",
      "Epoch 8/8\n",
      "13407/13407 [==============================] - 28s 2ms/step - loss: 0.6600 - acc: 0.6381\n"
     ]
    }
   ],
   "source": [
    "lr_finder.find([train_input_ids, train_input_masks, train_segment_ids,train_imagesX], trainY, 0.00001, 0.001, 512, 8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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JYUklNd7ad6JTFYSwGBrEAyEQCJrAHwlh1R61mDPQDEhlZby1V+MFmIoMyFmghUUgECaPKJMQVhCIsqYhVVEIqygbUsO9GkBlarwA8UAIBEIRUSYhrCCQFAUhhgJDw7cHIskKWKaC5MQEqWSgFKB5ILWcB0IMCIEwTZBkFWKNY+xBMpIVkMrVtm9iIoiyihBNg6oghCUpKhiaRq3Nu6L4HygFaOGuWib2iQEhEKYJgqzUPMYeJM/sO43fvtNf72XY0L0JhvIfwpIVFaFKyqMmiDYPhALls8RYk6Sv3XqIASEQpgmSrEI8i0JYoqJOSYMoKSpCxRyI3+WJsgKGplBrE6KoKhjaf5c86QMhEAgA9BzI2RPCkmSl5n0TE0GUFbC0/7t8QPNAgsuBUKB9ekehCuRYJgIxIATCNEGcohvuRJFkFfIUNIiSrBaT6BVoYRVzIOX44UtHsLdndMJrUxQthOV3WiI9VTyQ559/HkePHgUA9PX14dOf/jRuuukmnD59umaLIxAI40iKWnO58CARlalpELUqLBpMhVpYIZoqm0Q/k+Exmp/4gDxFVcFQ/vMzoaliQP72b/8WDMMAAL72ta9BFEXQNI3Pfe5zNVscgUAY52wr45VkdUoaEFFWwdIUaLoCOXdZ8ZVEF6XJ3QTIxTJebW3lj/cb6poovtV4T506hblz50KSJDzzzDM4fvw4wuEwkVonEAJClFVIZ1EZr1RMPE81tBAWXdHmqyXeyyfRRVmBMAkDoqqa8WAof+G1EFPbHIhvA9LQ0ID+/n68/fbbWLp0KRKJBARBqNq8cgKB4M3ZlgMRFRXMFPSoRL2R0OcmDWghLD85EFFWJuWBlEJYPkNTmhGc8NuVxbcB+dKXvoS1a9dCEAR8//vfBwD88Y9/xLnnnluzxREIhHGksy6EpUCaoh4IS9Og6XFdrHJozYflP4swSQOiSZkUQ1gea9PXPWUmEt5222245pprwDAMFi1aBADo6urCD3/4w5otjkAgjCPI/uLnx4eymNcaD2BFk0OUVYSY6m5uqqpiMCNgRpKb8DkkWfNAKglhyUWvpdzRoqxClCb+mVUVoCgKDOWtcSUr45VkU6YTffHixSXj8fzzz6Ovrw8XXnhhTRZGIBDMSLJS9m5SUVR87ZHdAa1ocsg1aCQ8lcrjW7/aO6lziEVhRK2M199rJJ+d6JKsTEqORi5KmZQrz5WKoouVzHWfCL49kEsuuQR33303PvCBD+Db3/42vve97yEUCuGLX/wivvGNb5R9fTabxUMPPYT9+/cjkUhg69atWLt2re04URTx6KOPYvfu3ZBlGQsXLsSnPvUpNDU1VfbJCISzDFFWioqv7jMhRGVyIZIgkRQFolzdENaJ4RwEaXKfX6+ooinKdwhLz4H4SaKLk1ifUhwoxZTR6dI61iktiT4VPJC3334b69evBwA88MADeP7557Fjxw7cf//9vl7/8MMPg2EY3HPPPfjMZz6Dn/3sZ+jt7bUdp/ebfPOb38Q//dM/IRaL4eGHH/a7TALhrEWUVURZxlPORJRVCNMkTyLK1fdATg7nJi33IpaqsPxLofvPgUxOjkbRq7DKeCB6XwpdQSHARPBtQBRFAUVROHz4MFRVxdKlSzFnzhyMjIyUfS3P89i1axe2bNmCSCSC7u5uLFu2DDt37rQdOzQ0hPPOOw8NDQ1gWRarV69GX19fZZ+KQDgLEWUFEZbxLOUVpekjd1ILKZOTw/lJlckCmmfEMv4rnYDxHAjgnXgXpcmV8WoeCFU2ia7PDal1I6HvENbFF1+MW265BX19fbjmmmsAAIcPH0ZbW1vZ1w4MDICmaXR0dJQe6+rqwsGDB23Hbty4EY8++ihSqRRisRj+9Kc/4fzzz/e7TE/S6XRVzkMgVEomk5n0OfIFAVyIwshoGnLE+U93JM2DF6Vpca3neRG0qlR1rUcGRlEQxEmdM53NQRIUZDIZyLLs61wFQUQhlwNUBamxtKs3UhBEZHOFCa9PEEVkMxnIkoixdBbpqPNxo1kBqiIjn8+Cn+T34YVvA/LjH/8Y3/3udzFjxgz83d/9HQDg3XffxVe+8pWyr+V5HtGo+ZNGo1HwPG87tr29Hc3NzfjGN74BmqbR2dmJT37yk36X6UkymazKeQiEiTDp649mkIiwiMbiSMbDjoeMSgxklZoe1zpNAwxT1bUO5WRwbGhS5wyxKSRY7fdi/K6PYtDYkESYDSEaiyPCMo6HKaBAhSa+PpoJobEhiSjHIRKNuZ4nrxbAhVk0JpOgmMl9H174NiCtra24++67TY999KMf9fVajuOQz+dNjxUKBXCcvdTu5z//OSRJwr333otwOIzf/e53uO+++3Drrbf6XSqBcFYiKqoWwvIIgUg+S32nAlINciBqcV7GZJAUFRG2Mp1ZvRO9XN/FpHMgxuoqrxBWMYnOVCDHMhF8f0uiKOKOO+7AwoULEYlEsHDhQtxxxx0QhPITxdrb26EoCgYGBkqP9fT0OMqg9PT0YP369YjH42BZFpdeeimOHTtWlRAAgTCdESUFsTAD0WNDmGync5CISnW1sAqiDK7Cjd8JrQqrsvPIila5xdC052cSZWVSVWKlKqwys871ct9KFIUngu9v6dZbb8Xvf/973H///di9ezfuv/9+PPfcc7jtttvKvpbjOKxYsQJPPPEEeJ7H4cOHsWfPHqxbt8527Lx58/Daa68hn89DlmW8+OKLaGxsRCKRqOyTEQhnGZKiIFrGA9E6nadHFZYkK5CruNaekRxmN8cmfR5Rrny2h1TcsEM05fmZqiJloveBlEmiV6LaO1F8h7D03ozW1lYAwJIlS7Bq1SosX74c27ZtK/v66667Dj/5yU9w2223IR6P4/rrr0dnZycOHTqE++67r3SOP//zP8ejjz6KO+64A7Iso7OzE5///Ocn+PEIhLMHQVbREPFTxjs9PBCpyuKQJ4fzmNMSxZEzk4tWSErlHoguf1Ku81urkptcGS9VNAxeoalSJzozRbSw3ErT/DbaxONx3HzzzbbHu7u7TQYokUjgpptu8rssAuF9gyQriITLlPFOo6mF1Z4HcnIkhzlV8kBCFXogiqr66s9gGKoqIUY/fSB0AB6IbzN77bXXYsuWLXjmmWewf/9+/OY3v8HHPvYxXHvttTVbHIFAGEdWVHAh2vMOVpQUKKr/Brh6QqG66zw5nMOclskbEEnWpEwmQrkRsixDT8pD1M1auQZBUxJ9KuRAvvOd7+DDH/4wvvjFL2L16tX40pe+hMsuuwz33ntvzRZHIBDMsIx3klbfnKZDIj1E01Vd52heRHOMnfR5RJ/DoZxgyuRAwoz3Z/7GL/3peDFlBkoZk+i1FFP0DGE999xzpn9feumluPTSS01aPC+//DI+9KEP1WyBBAJhnBBNeYao9PyI3rU+laEo+B4Z6wddggSAp15Y2fMUR9pOhBBDeYYYWYb2zGG9enjI1/uUq64yJtHrJuf+2c9+1vFx/YfRf6QjR45Uf2UEwhRBUbT49lQgVGYDEkseyNQPYQEoKz5YCYKsSZDoAoKV5jF0pAlUYel4ScCrqnZeNw+kIMpl56XrZy6nsisrKhhGq9aqof3wNiBHjx6t3TsTCNOAvtE8vv30u/j+dSvrvRQAAEt73+GK0yiEVW0kWUGYoUt3+aEJOmAT6QPR9+iQR8hIUlREw4xrH0i6IKEgyr7ez1cfyCQbKv0w+a4bAuEsJifIONA/dZpYQ0yZJHrxuclKmgdFNW+OxWLye7KJam0eyERzILTrxi4WDZwb6YKIgih7Vrb6TqIXcyC1hhgQAsEDSVZx5EympnIQleAVAgH03Id3ov1sRSxOEiyXqC6HVJy7Ugn6Vu01f0OUvKu7xgoSFBWev53+TDmJEmJACIQpgCgr4CUFp1L58gcHQNlGNVlBPByaNiGsquZAJAUsTZc1sgDwv561K4HrSBPoA9Hx+n0EWQEb8jAgxfyHnzBW2SS6SkJYBELd0UaDAocn2d1cLUJl7q4FSfGMs081qu0n0TSl5UA85o6rqoon97rPGNJH2k4Er8Y9UVbAengFYwXdgLj/dqYQlo8keq0hBoRA8ECSFcxvi+PImWy9lwJAS6J7bRyirCIWZt6XISwdNuSdA+ElBVlBcn1emmQfiFuRQ7kGxXRBQoimwEvuHogphOWjjLfWEANCIHggyirOnZmcUh6IdxJdQex9GsLSv5VyOZCcIIP3uMs39pNUSsijwc9PCKstwXl6IDqap+P+PMmBEAhTAFFW0D0jgWND9fVA9MoclqEglinjjXMMxCkewlIUFbW6QS6XA8nyEniP70cfaVsJJc/Ao5GwXBXWWEFEewPnmQMphbBIEp1AmPpIioIYF6p7TkFvjAvR3h6IUPRAglLkPTaYnZB4o6holU7VDLTp2yVbxgPJi7LnJi3JasV9IDpeM8hF2dswjeUlzEhwnsZNh6HhSwur1hADQiB4IMoqQjSFZIQtJTnrtw4aoTJ315KeAwmoE/2+5w/h8ATyQ1Lxe60FLEND8Eii6x6IW79FuY3eCWNy2y3/JJYpDx4riJiR5MD7qMLylUQnBoRAqC964nNWYwSnRwt1W4d+x84y5ct4g8yB5EUZeZ/d00akYp6hFltcuEwSPS9o6/W6069ER0sfMwt4eyBCmT6QdEFCe5JDwSOJrkOS6ATCNEBStOa0WJhBTqh8o6wWoqTdFTM0XUZMUUE8zAQWwiqIcmlDrgRxAnmGcujbKctQnjmgrA8DUgli8RoBAIYp04keojURSYdjsryE5njYM8GvU27OhxyQfhsxIASCB2Jx0lwsHELOo/Sz1kjF3gS2TCOhIAUbwiqIim/9JiPGPIPfoXR+KV+Fpf2OXuWyRsqtT1bGw3F+ciAsQzsWQqgAIizjywOhfQyUqlWI0IjviYSTJZvN4qGHHsL+/fuRSCSwdetWrF271nbcD37wAxw+fLj0b0mS0NHRgW9961tBLZVAKCEV7xpjYWZCd9rVQpC0u1w/ZbzRgENYE/HMdM9OC8UA1XBGSkn0MiGsnCBr/RY+7vRpqvz6tHnomjH06kTXcyDh4m/IOey+EZb2LOMtVXuVkWkPKokemAF5+OGHwTAM7rnnHvT09GD79u3o6upCZ2en6bhbbrnF9O9t27ZhyZIlQS2TcJbw+Js9YGgKW1d0Teo8oqIixtB1D2FJioowU0yi+ynjDcqACJPIgZQGHilg6MnNLjF6CeVmbuQEGU2xsC8PRB8d67UZGwsCGMq9jFco5n3cqsQoAJEQg9Fc+WINbV3uzytnUxKd53ns2rULW7ZsQSQSQXd3N5YtW4adO3d6vm5oaAiHDh3CRRddFMQyCWcRY3kRI1lh0ueRivIT0TqHsHShQLZMGa9YKuMNKoRV3oA8/+4A3utPmx6TikObtAFZk1+rZJj/UTaExUtoibO+GvZoHyNhJUUpyYaEPIocNLl5bV6J1UOSZAUMTYFjaRS8kvs+1yUFlEQPxAMZGBgATdPo6OgoPdbV1YWDB90FzQDgtddeQ3d3N1pbW6uyjnQ6Xf4gwlnBWDYPSVEn/ZtncwWIEQq0omAwLUz4fJnM5DrZR8cygCyDL+SQK/Cu68jzIihZQDZXCOR6zwkSUums53vtPNyPRTPimGUYV54ay0CVJaiKgtRYGkpkcltRTpBBqQrS6TQkkUc6K7quKZXNo4FjMDKWRrrBfg8ty3LptYosYXQsDTHs7iGNjvGAor1G4Hnkcs7vnc7mwDI0KFVGajSNKMY9jVRORDQEKCKPsUzede1ScW18IY9c3v03zuUL4DltzzN+nmoTiAHheR7RaNT0WDQaBc/znq977bXXsHnz5qqtI5lMVu1chKkNxbAQFXnSvznNskgmYqAA9GXGJnW+ybw2nJIRi3JoakiAYkKu51JAo7Uxgb7M5D+7H3hJgUKxnu+l0CFIMK+ZG1UQi3CIFhREY3Ek4+FJrUPJi4hyYSSTSTTEs0jxOdc1SSqNGQ1RMGzE8RiGYUqPcyyLWDyOZMR91npKZBAJa++djGeQkSjH89LsMJJRFjEuh3AkhmQyXnpuWMiiJRFFc0MSKuO+dn1tiTiPEFtwPS7EhpGMx5BMJhEKMYjHEzWpygpEDITkAAAgAElEQVTEgHAch3zeLIddKBTAcZzraw4dOoSxsTGsXDk1JsERphe8pCDLTz7kJBWrsMIhuq5JdLEo8FeujFdRVURYJsAQllI2hMWLCtIF828hGpLoXhMW/WKUCWFD/nIgfqudyulSSoaKJ8+BUlKxlydkbwYdy0toiLLgQrQvKZMoy3geJ6vjZby69Hstum4CyYG0t7dDURQMDAyUHuvp6bEl0I289tprWL58OSKRSBBLJEyQ0bxY143VDV6SkamKAdFKL2NhxlPBtdbo0/b85AxYhpqQvEilqKpaNKze3wsvyUhbuvh1w1ytHIieIwL8iSm2xFmfVVjOPRtGJMN7e4201fpAKMeJiemCiGQkpJXx+qjCaoyynvPTFcWc2Pcq+Z0MgRgQjuOwYsUKPPHEE+B5HocPH8aePXuwbt06x+MFQcAbb7yBDRs2BLE8wiT4j1eO4dl3++u9DBvV8kD0ypl6l/EaewjKSbWX04KqFrykoDnGlvVACqKCMYsHom+6XnfslWCc9lfu8+cECS1xv5pT3oObAK0xMV6syfXqzzCW8Vo9pLGCiIYIiwhL+6oOa4h6S+sYk+iMx5omS2CNhNdddx0EQcBtt92GBx98ENdffz06Oztx6NAhfPWrXzUdu3v3bsRiMSxevDio5REmyPGhHMby9bszd0OQFGT5yW/44x5IqM5lvNrmE/JQe9UpV8ZaLfKCjOZ4GPkyd/KaB2INYam+pFn8oku9AJoH5illIspoirK+GiDpMv0WAJApSEgUDYinlIlhZrt7CItx9YxUVRtuBvjzQPQQlpc+12QJrA8kHo/j5ptvtj3e3d2Nbdu2mR5bu3atY5NhPXnj+AjaEmHMa42XP/h9xInhLBZ3JOq9DBu8pFQnhKVoHdPROveBCFKxh4D2Ng4qym+g1SIvymiJhct6ZgVRgSRbQ1hKqQ9ErloORNswy08kBKJhBpm0/fqwdp3TlPt8D50Mr4WfAH+NhNr6LAakIGJuS8zTA5ENmlvlbhLMeZnyYbiJQqRMfDAwVsCtj+3Ggy8frfdSphzHh3J1Val1g5fkqvRtiIYcSH2lTMZ7CMrlN1jGO9FeLQqi7oF4fy8F0Z6P0oc2hWiqKt6SKI0PgQqHyofw3JLVeq5Jx08Ia8zmgbjMA5G0RD8boiAq1hCWhGSE9cyBKCp8Nwdak+i18kCIASmDoqj4u8f24J+vXY49p0YhygpG86Lr3W21dX2mMvqdpzU8ESQ7jgzh5HDO9jgvektn+0UydA/XKo7sBy1JTHs2qukEFsISZSS4UNkkuKSotu9OH9oUqtL3KsiK7xwIAC1U5JADkQzCiIDugZQPYfn2QEKUlgOxeiB5EQ3RUNGguhkQ1aQS7GVKrEn0cmG4iUIMiA++9meLsXJuMz54zgw89+4AbvrRTjzyp5O243791ik8+MdjwS+wTpwcyeH8zoa6GpA/vHcG+/vGbI/zkgLOY3yoX0Rl4vOxq4l+Z6yFsMp7IEGEsAqijKhHg52O07eniyl6VS1VgjmERZUdAOYWKhJl8zApmkL5HAgvlfpEGJqC7GJQxWI41DEHUkyie8nIK4YcSOkxt653Q7jrrEiiT1domsKy2U0AgI+t6MTXHtmNdQta8e5pbdM6cDqNN46PAAAee6MHrx8brttag+b4UA7ndzbaSjSDJJUTHauA9DvSyXqEkuw9wyEo9FBaub4ECsGV8RZEBVGWKTtV0Ol5UTb0gVRhrcbfKezDgHIh51CRXjSh42fzTRfEUgjLK+RVCmE5rE9PonuhiTqOry0ZCSHjElZVDNIu5ZR7J0P9/zKmEQtnJHDfX67C3125BMeGtLDJr986hW88vhc9IzlwIQaDGe/u+rOJE8M5nDeroSrJ6okymhdck9vlaur9YA1p1ItKDFmQVVgR1v8WYjTmutx4NT0QNlQ+hCUUm/k4Lw/E8D37aSTM8BISET0H4j0PhA1RxamS5mMy/LgRckNWzCGshijrKrxoLOMNTfc+kLOJSxbPAENToKDdrezuSeHK8zvwhYfexNYVnWiIuP+oZxsnhrKY3xZDPdM+qZzoakASXGjSxk2fB1Jv9E50wD32rW/QQfWB5EUZUZYp299MAeBYc85BVIpJ9CrmQPTvJ+zRiZ4TJMTCDCIu5bJiUTxTx08IK+07B6Ia+kDM7+0nQa5aQlgNHmOWFUMSvbs9hnAVwrlO1P8vY5qyoC2OQ2cyEGUVX7i0G1yIxofP68D5nQ3Y1zda9vX/8PgePPZGTwArrR3Hh3OY2xIrf2AN0TrhnY1EnGMm3Uxo7DJ2myQXBKKslu6w3ZCLYQs/OYBqkBdlRHyGsJKRkGmz01WOq1UhpE/7A7wNaE6QEQszrqq3RlVfwF8Xd4aXkOS08FPIIweih1UnauCtRsarF8Q4UOqv1s1GZ1PU8bjJQgzIBDl3ZhK/fPMUls5qQDTM4LEvbEQ0zOD8rkbsO2VP6hpRVRXvnk7jD++dwS+msRHJFpOH9aw7c/NAKADxKnggxg3F77S4WqDdGXv/uep3uJXM854MfDGJ7lU5pNMQCZmKLSRDGW+1cyBeBjQnSIhxoaIHYv8tpWK1m46f/IExlFeuCitcbJ6ciIE39oEAmgEZczEgkuXYWkEMyAQ5b1YDHn2jB6vmNZsev6CrEW/3ensgJ4fzmN8ax93XXICn9vbVcpk1hcJ4SKVed+YZXnI0ICqqFcIa37jjdexGtyZ3ndDCONX7k+4ZyaFvNO/62+ZFGZEQg1jYXdhP2/SAZIQ1GRCzmGJ1y3i9DGhOkBFj3T0QLWRpLuP1E6LV31O7aXHe1EtyNJYQm6KopjAgRTm3A6iqCuNHa4iyrioQ6YJUklepJcSATJBzZzZgOCtgtcWAdDZG0JvKu7xK440Tw1g1r1n7ozJscKnc5AcgBYU+AAfQNup6CA2KsoIEF3LthE5woUmHsBQVpVhyNMwgVwV5lIkgWJK7TuhifdXia4/sxjce34t/edZ5bk9eUBANM4iwjKseliApiLAMklzIdLesiylWq79G9GFgASDLy4hxIYQZ2tkDUcweiJ9GQiNNURYpt8S2rE0JDFsaPTPCeA4FcO9R8RvCUlUVQxkeLZOUyPcDMSATpDHG4i9WzUZno1ktmKIoXLSgFf/w+F7bXdnzBwZQEGW8cXwEq+cWDY+q/eBHB7O45ae7glr+pMny4wJySUt4IijG8iJmNkaQc9m8qhHCMhILM8iV6bquFUYPxG07E+XqNE/qqCrwrauXYiBdcHy+IGk5kCjrLjRZEGVwIdp2jYyLKZYPf/lBl0ovR17UkuhuszGsneh0hRVMNE15hnQpirLlQMbyWg+IjluXvKzaQ1hOBuTgQAbntAcz+4gYkEnw3U8sd3SXv37lEiyb3Yhtv3uv9FhOkPDNx/fin585gAOn01gyU/uBdTf0yJkMDvRPn4mJGUFCgtOayKzhiaBI5UXMaowgZzESerWK5oFMzmMw/rr1FFT0YxyMirSThZdksCGqGAZ0/sx67D8advdAdCOjXSPjm50mpkh5ig9WgqT4++xZXka82Pzo9K72PhBvdYmJ9BlpWmXjr7P2gERYFw/EktdoiIYcq7BeOzKEixa2VLyuiUAMSI24dvVsvF5sMASA/97di89tWoiekTwiLFNyRWc3R9GTyuHoYBZjeXHahLFy/HiM1e1CrjWpXNGAWDZ1vng3Gq9CCMtIPSXd9Q3X+5iJeSA/eM4eojo9WsCsxqjnd1golvFGPb4XXlTcPRBaK+OtXg6kfAgrL8iIht1zA7p4pk45DyRnkHLXiYe9q/9sHkhBNIWwIqyzB6KqMJXx6h5IXpBx+Mz4yOQdR4dx0cLqjAEvBzEgNSLE0JjdHMXxoSwA4PE3T+GaVbNx1zUX4JbLukvHzW6OomckjyODWXzwnBl4r995dvYDLx7BJ+5/Fb/adSqQ9Zcjw48LyFnvLoNiNC+gNcHZYtSajAmDOMdUPYRVTYNUCZIxSexyjFHOoxJ+9Mdjtg3r1EgeXU1RxFh3Ecl8sQrLM4Tl5oHIWnVbtTwQv95XVpBKHojjeWS7FpZXDiRtEFLUmZHkcCbt3lBsFXtMFyRLCMu5AdYphDWWF/H02334+1/sAaB5RH0p7bcLAmJAasiHzm3Hc+8O4JVDg5jfGkdjlEVbgjPdHXQ1RXFqJI/jQ1lcsbQd77mEsV46NIh/vX4lHtpxPKjle5LlZcTC9c2BjOZFNDnIP/CSDI6lq5JEN+IVqqk11g5px2MmEMLiJRlDWQH9Y+Y8x6niJkTT7lVIJQ/EI4leEItJ9EjINFRKn29SLSkTv/mfnOCt36WVA5ulTLyiVEYpd522BOepSOGYA4maPRCnLnljcyCgjbXNCTJeOHAGgqzi2GAWe3pG0d0e3HgFYkBqyCWLZ+BXb/Xi3t8ewN9/5FzHY7qaoziVykOQFJzf2YiDBgPyw5eO4O1To5AVFXlBwszGSPEPuv6KvxleQtyQA7FOnAuCVE5EU8zBgBTDJtVOoldSxitISlX1qARpfOa36zEGOQ+/DIxpG11vysGANHvfxeaLxsHLsPLFJHqDJU+miSlWWcrEh/eVE8ZDr44ij4piE1N08pCOD2XxH68c0zyQSGUeSIimIBjmlehCijpuEjyKYu5Ep4oquz0jOXzl8m489kYP7nn6Xdxy2Tmu711tiAGpIU2xMBbNiOPuay5Es0tJXVdTFO/1pxELh9DdnsDhM1rIi5dk/Merx/D4m6dwcCCNczq0pHu5SWQT4Yk9vRWfM2sIYWlNYvXJgTgJ0AmyZkD89oH8fOcJX2GUSoZK/Z/XjuOpt08DAJ7e24ffvzvoeXxOkDzLvwvieLOaW1zeeBfud0vuGy2gNR7G6THze+shLK9z6RVW+p2w4zHSuAdiDWGxVZQysVZPuZ0xJ2hek9sxosUDoV3KeE+l8vj9/n5NxsQphOXhgVhDWGP5cTVfwL0KS1Fhaw4cygpYNrsJm86Zgcff7MHGRa2Y2xqcOkRgEwmz2Sweeugh7N+/H4lEAlu3bnWdOnjixAk89thjOHnyJMLhMK688kp86EMfCmqpVeV7n1jh+XxLPIy3T41i64quYvWFduH8dl8/rls7F7/f34+FM+JYVSz7ndUYwemxAppi5Wu8Vcv8ALdj7nn6XfSm8vjcpkU+P5UWS24uriEZcW9oqiVaCMv+PWgeCOM7if7jV47hkiUzMKvR+447FmZsFV9uGFWC9/WOYXAsh2s8hmy+engIO48N4x8+cp7j83xxIwZQFONTwNDmUIw1B+Ln9z89VsCKOU3oGzV7IH2jBcy0lKg7QVEUomEGw1nn4g/dyCQiZmMuKVofUVXl3H14XzlD+TlDUbbOc1sfCOXs8ed4Gfv7xkxS7jptCQ7vnXavqHRKoptDWG59IKpNLysZYXHJkhkIMTT+9fqVuHB2o+v71oLADMjDDz8MhmFwzz33oKenB9u3b0dXVxc6OztNx2UyGfzgBz/Axz/+caxcuRKyLGNkZMTlrNMfiqLQmuCwcIY2Krc5Fsa+3lE88vpJfPcTy3F6tICHdhzH9r9cBQDoaIjg9GgB585s8Nwg/vPVYzg5nMM3P7rU8/2PDGaxYk4Tntx7Gp+9eKHviWdZXsac5vp6IKN5LYSld8Lr8WFe0jatmMedsZGBNI/eVN6fAfGZA0kXJAxltWN7U3kcOeNcHKEzlBGQyrp/h0YxRbdNVzRshixDlbSxvOgf1QzIaYsBEeRxg+W00RrxSqLzkgKOZbQpiYY16xIx1cyBGOe26BpW1us5J2paWICWaxAsn0uUVdN5NG/P/n5ZQcJgRsDRwSxmNpgNbTkPhGUoSxLd0gfCOnf2a2q85se2LJuF9Qu0nOqa+cGU7hoJJITF8zx27dqFLVu2IBKJoLu7G8uWLcPOnTttxz777LNYunQp1q1bB5ZlEYlEMGvWrCCWWTe6mqJY0KYZkM9snI8f//EY1i9sRXsygo9cMBP9Y4XS8zMbIugfK0BVVWz+/ksYcrhQ84KMx988hd09o2Xl5V987ww+fF4HLjmnDb/f3+97zVlDGW/d+kByAppirE2jSh8m5dYsZoSXZAxnBZxKOTfLGYmF3bverWR4EX3Fc/aNFqCo9ql8RgazPIZc7uJ19JuFEEM7CvYJklEPyp+k++mxAlbObTZ5IFZpjTjHIOvxuT37QEQZEQfPQCpKxPiZsOgHwZJEZ13G2uZ4qWRAnKqdrLL5blpYWV7Gwhlx7Dw6bEuil8uBWH8bax9IzMUgqw4hrL/+4EJfQ71qRSAGZGBgADRNo6Ojo/RYV1cXent7bccePXoUsVgM9957L2699VZs374dw8Nn95CmP1/VhQs6NddzY3cb7r12Ob5YLPVdt6AFd2w5v7R5zGyM4PQoj97RAnhJxrd/867tfP/56jFct3YOPnvxAvzoj95z3F8+OIiLz2nDtWvm4Mk9/nW5jEn0hmh9PJBsMZ4ds+QmBMlfOAPQksjNMdY1/2DcOiqZi54uSDhdrGySFRXdbXEcHcy6Hj+YFjDisweIZSiIDnO3jSGsEO1vKuHp0QIWz0yYRhAMZnm0JbjSv8tVs3l6IEXFXitSqYy3OjkQSVZNv7nbUClNjVfb8J1mgljnvzAuulRZXsLaeS144/iILYme5EK2ohKjN1SuD6QlEcZg1m6AnEJY9SaQEBbP84hGzeGBaDQKnrd/SalUCidPnsSXvvQldHV14Ze//CUefPBBfP3rX5/0OtLpqdnp/aFFDYDCI+1y13J5d0Np7QlGxonBMbx1dABbL2zHgYEsXnynByvnaAborZ5R/HZfHx741IVgaAo/ePY9fG5Dp6MypyApGMkWwKkC2JCK3pGs7+8olckDIo90Og1VVZHOC4F/v7IsI5PJgKUUnBkZBadq11gqnQVkCel0GqIkea7r6OlRXNiZxLGBMdtxiqpCkeXS44ogYDTL+/qcI9kCGKhIjY5BUWQsbGbxxpF+dESdN8vTqQxGPM4tG9ahKjJSo2lwqtngpLM5yJKCdDoNSpWRGh0DLXnnyvpHc+BUAZI8/j0dOjWGGXGm9O8wrWJgeBRJxrwp6t+NIvIYzRYc1z6azaOBVbXjDJ+hIIgo5LLgC3lk8+bPLRdnqFcywyLPC+DzOaQpzRBSqoyR1BgoS/GKKEnIZbVwIqMqGEqlkaDHP1cml0dDSC2tRxAEZHKq7bONpHM4f2YUD78ugZbt177x9wKAfb1pNHJ06TFelGzfhe4DxmkZvcMZ2znT2SxEsfK/s0zGO3w6GQIxIBzHIZ833+EVCgVwHGc7lmVZLF++HPPnzwcAXHXVVbj11luRz+dtRqhSkslg9GFqyaJwFMP54+gZk3DB3DZcv2ERbvnpm/j559Zj18kU/vUPJ/DAp9eVhNQWtieRUVhTY9FwVsBAuoC7nzqAT61fUPpeaJrx/R0JKoX2lkYkk9pvSDP+X1stQsX3bIxHQbPR8c/BptGQ0H7vKMeCi8ZdN6O0lMHaBW3Y3ZOyrZ+XZETCbOlxNiIjJ6m+PqcKGi1JFkMCjZlNMSyf14qXjo65vnaMV9EYC7s+zxi+3ygXBheNIZk0V9uE2BQ4jkIymUQsEgYXjSOZ9E6E0zSDxoYGxLgwwtEYuBCDvJpDZ0ui9H7NiSjUEGdam6qqpd+8TaAho9957XQIzQ3auYyfQaVoNDc2oCELMKFR02uf3d+PZ98dwN3XXGj+TlVVC+M43IWrFI2mxmQpoe32+Y1rSMQ4hLiI6b2ZUBjJRKz0WCwaQZgL2T6bCBrL5s1AMnIEM1sakUzGbe+TSCRAURRGsgK+/ewe3H/D6tJ6GHr8+VDIfP55KovR10/b3jMaFRHluAn9ndXqbzOQEFZ7ezsURcHAwEDpsZ6eHlsCHdBCW8bEcFCzDaYLmvKtjAP9aSzpSGJmYwSbL5iJv398L+79zQH8+1+tMalwLmiL45ghdPL8gQF85kc78cCLR3Hb5iX4+OrZpee8RmRayfKyuQNXnZgu0EQxSoxbQ0t6HwgAtMY5DDmEA3T6xwo4pyPhWEVmbSqLsExFcxw6m6J449gIupqiWNwex/4+9zkxgqwgEmJ8JZSdJtrp59D7QEJ0+aFFiqKWmiE6GiKlnpBUTkSjISYfd9AUE2W11JcSC4dwbCjrGMbU81GAVr6qf396SMcpBzKSE/Ffb/WawmYPvHgEV/+vl3Hnk+84fhbBsB7A31TGCGufSigpqk2N12kiYVbQrv/zZjXYQliANi0wlRPxx0OD+Ov/fB23bT4XHYZke2dTBKf0sKnl9C3xMIYdQ1j2HEi9CcSAcByHFStW4IknngDP8zh8+DD27NmDdevW2Y7dsGED3nrrLZw8eRKyLOPpp5/GokWLJu19nG30DOcxu9jo9ZmN89EUZfHvN6629ZssaIvjiMGA/PfuXnzvE8vx3U8sx/md5pK/ea0xHB92j9MbMfYlAEBHYwT9Y8HNgzfG6a1aTLqUCaCVVA5l3HML/WMF0x+2EclH97cXXU1RvH58GF3NUcTC2mblZYCaYv56fJpiYYw4GHpjDiQcosrmQIayAtoS2vUyqzFSSqSP5u0GxNpPkzfkNmYkOdywfh4+9cBrODmcMx1XMBwXtciiUJRzDiRdELG0swFPFmflqKqKp9/uwy++sNFVqUGyJtFdciBGnPotHEfaOpwmV8wB/uBTK035Ip2Vc5vw+YfewG/3nca/3bAK6y3aVItnJvFefxr9YwWb7LpbAYRsaSScCgTWSHjddddBEATcdtttePDBB3H99dejs7MThw4dwle/+tXScUuWLMHWrVuxfft23HrrrRgYGMBNN90U1DKnBfodne7Khxga37p6qWNvyIK2OI4WmxMFScHxoRy6XaSe57XGcHwo5/icE0bvUDNU9lir0Sv5h8f3VM1L6UsV0Nmkbfz2JLpcClm1JsKeJZW6AYk66Fxp4oT+/2J/8UYPdp0YLzmf1RjB68dG0FksD16/qBWvHLY3FOodxs3xsGMi3TpIyK3Kx9hIyDI0pDJVWEbjObMxgr5R7Y54NG9u0Ew4jAbWpxHqbF3Rha0rOvGOxcvSpUyAoidjSbZrcu7mdWYKEm7cMA+/fFPTfTs5nMe81njJY3C6hqwlu2GmvEw8F6Jt/RbWmwY3LaxMsZ+k3SVE+MXLuvHI5zfgf269wPGYJR1JvNefwZ+ODWPtAn/lt1Ypk6lAYH0g8XgcN998s+3x7u5ubNu2zfTYpk2bsGnTpqCWNu1wu2idmN8Wx7GioOMfDw/iA91trsfObYlhX6/3OF4d65/UwmKV0cZF5vN/8t934MHPrEWmIOFnO0/i63+2BK0Od2yVYuzbiIZDpv4MY9hkRoLDoEdJ5UCax4wkh86mKPpG8ybjqsltmO+xaJf+AgAlA3phVyMYmkJnUxRHBrOledSbz5+Jn+w4hkuXtJteN5IT0BwLozUexrBDL4jRowJ0A2IvOzZ2Y/sJYfWNFko9DLMaIzg0oK3f2qAZ50K2RsE0Py7nrzO7OWbzQPSeHEBXMzYbIk1M0bzONC9hdnMMrYkwjg5m8dbJEaya2wRA15kSMCNpvoas12NDlMWI5bs0DkEDdMkQiwdircKincfP5gQJMYfqMr8s7kjiyT19OD1awJ+v6rI9r/eKmLrr368hLEJ1mdnIYclMf4JpDQYV1Kf39uGqC2e6Hju/1ZwvqYT5bfbXDqQLeOtECr95+zSefbcfrfGwreN5ovSOFkobs1Y3b8iBGENYyTAGPUJY+hyJrqaIrRfEqswKeM+hzgkyhrJ8SalYL1zQ/3verCTePZ22hWyGsgJaE2E0x5xj33o3t067iwciSOPrZX2EsHpGcpjdrCXiWxNcqQ9lNC+iMeYdwhrKCGiNmzfxOS1RhxDWuAeiqRmbN2ynHEi6ICLBhbBleSee2N2LN4+nSqOjF3ckTHpxbpzTkcShAfNxxiZCQGvatXqn+qREHS0HYj+/rEw+vNkzksc7vWNYOqvB9nxr3B56fV+HsAjV40PntuND53aUP7AIQ1MYK4g4fCaLJR3u1Ri6sONEWNBq73N483gKn/nAfPzX7l48t38AH18z29WAHBpI47932/uC3OhN5V1DWLoaL6DnQMrnZjqborZeEEkxbyaAlqdIuRkQXsZQRtD0kSIsOhoiSHKhkkwFRVFYO78Ffzpm7msaTGv5nBYPD8TYS+HW6WzMN4R9hLBODOcwt6VoQOLhUtOpNQfi1AcylOHRmjCHTOe0xHByxFJtaciVOQ3kcs6BSGiIhHDJ4hn4w3tntAFsxet28cwkDvSnkS6IpnChlSUdmrE2kjf0gADAubOSOGA5RrLcNFBl5NwnCk1TAAVEwoyjIXJS9J2KISxiQKYhq+e1VCTZPLclhn//wxFcdeEsz6o2P3FzQB8GZLkzj9n1sHadGMGfLe1AmKGRFSRc0NlYirMbOTGUw9cf3YMHXjriuyGxb9QYwnJoJGT0HIi7tHa6IJaE8BwNiJMHEmNdh37lRBmDJQMSQjhE49Mb55u+81Vzm/H2qVHT6waz2lwTtxyI1QOZkeRKFVNGxgrjG7+fKqQTQ7mS8F5rIly6483w5pkZ8bC9Cktfs5GGCIuMpYHO6A0aq+X0q0yTMrEbkGREUxiY1xoHG6JKm6yeO/jPV4/jhy+NN8lar+oFbXEcOWO+ockautABoLs9gYOW+TvWwV0MRZkq/qrJvJZYKTRnxSnPJSsqGBLCIgTN/LY4frLjOK5ZaY+1WuFcpqEZMc5DN0LTMJWh7ukZxQVdjfjL9XPxfy3vQmfTeKWPMRH6/zzxDv752uW4ccN8PLTjhK/PpIVQtDtgq8SIpr+kl/GGXSVC+sd4tBdzAE0xrezSiFXhFQCaomFXDyQvSBjM8JphKpZ2fv3KJaZjGqN26fuhDI+2eBgtsbCjKKExDARonc5ph85woyMXh7gAACAASURBVOcQC5cfppUuSIbjQyY5EqPRS3D2eS/6mm1YOrdNORDO7oGwDAXJkgPJG7yWT66dgyvOG/e257fF8V5/Gr97p7+s4q2kmBPuOYsHwoUYiLL5GH1Sog5Dw7GMtxpcdeEsbL7AOaTc5lD8YS1umAoQA/I+YGFbHBd3t9nKBZ2Y02JPhOqoqopXDg0W56HbDcic5hh6iiEMQVKgqCoiLIPLlrTjUxfNxczGKPqKd/mf/8kbpbvRsYKI7vYEtq7oxDP7Tpc1YIB2B6u787YQljh+1+umbAroVUjaXbRTmEabDWG+42uKuffK5AQZWV4qKrQ616c45VAGMzzakhya4yxGHAwIL5lLpt28yNH8uCifPunSDWMPiBXrw4mIUwjL7oEA9o1PMTT+OU03ZFy0pvTPuG5BCz7zgQWlx1mGRqYg4aMXzjJlzp22+JmN0ZKcDKAbEHPiu8vyPekSK8Z1WNdXrUrCy85tx7kz7fkPAGhL2j3nkZyIZof5N/WEGJD3AZef14H/95oLfB3b1eSeB+kZyeMrD79VFFK0V6DMN+g97esdtfWZtCc5DKR5yIqKlw4O4uRw3vTHyDI01i9sxV5LiMeKIJnLazUxP2MS3RzycaNvtFAqsU1yrO2u3mkKYFPUPYQlFxWBtTGnzn/oDdGQrddD96Za4xyGHUNY5iosACUFYut69fJlp4ooI2cyPNqTdgOgqqptM9bEFC0GJGvPgQDaTcTJYefrJ85pht6YDNaqxczvWC5I87lNC/HJdXPAsbSnuOWSjoQpx5ETJMQs1+15sxpMuRJRMXudDGVvJNQVhmuJVj1ovhY08dDyN4FBQgzI+wCWoX1feF1NUdt0Op09PaMYzgp4rz/tGMJa3JEsNXrtPpnCCkt8V5f0PjqYgSArODGc0yqQDJ7RijmN2H0y5blGa/OfrQ/EMhvCbapcXypfmnkRd+h1kBym3DV6JNF10gX7mNPS6x0Ggg0W7+atDZE61qZNQKsg8lqHkydp/PfxofEEuk6CC+FMhrfdpXMhewf+cFYozYOxvm/PiLPh0pPoYwZPyckDKXd//xerZ6MhwmK2oejDyegs7khaDIhsK709d2bSpBBgze81x8O2aqgs7z1XvRo4FUro5d5TCWJACCa0SiznDWDvqVFsvmAmXnpvEPGwfYNc2tlQaiTb1zuG8zud3fO9p0axcVErjg9l0TMy3lEPAMvnNGF3j7cH0pvKlzwHAIixIYcQ1vil3RxzTk73jY03I4Ys8yoAfTaEQw7EJYSlQpuN0jtacJS3AJxDZdpIU/eWLGsVFlBMpDv0gug0Rs0S+zuODOGqf32p5LWcGM5hnmVyXWs8jCNnsqYKLDcUFY69MF4hUD2JnsqLpY1Qm20ybpwqKVWd3exurABgyUwHA2K58Vk6qwHvnh43IFavc4GDinJOcM4BVhMnORMSwiJMeTo9PJB9vaO44aJ5ePnQoOMfUGdjpFTJdHwohwWtcdsxrfEwXnxvEFddOAsnh3OmXgRAm3dy2qFSy0ifoQcE8JYyAZxLIgHdA3GXyLE2lQF6st29r6QtweHomayrQXDMX6jjjzvdfVursAB7L4h1hkfp1KqKgqjJ/q9f2IpDxcFWJ4aymGPxQFoTnG8D4sac5qgphGVcU6xYzTVSnOMCaPkRY4Qo65Jfc8IYbnX63uY0x9BjCMfmBMnmXVmrnayz1ZsdiiuyQu09ECc5ExLCIkx5OpKcbTodML4RrZ7XjMEMb+tCBrRNMMIyGM2LoGln1dRZjVG8+N4ZXLG0A8eHc6bZ2/o5mlyqkXROpfKY1TQewrLOmBYsm0CbQzwZGBfEc8Mqpgh494EAWjnssaGsaw4EMG92vEF2BXDWZ3KKuVtLeZ0KG1oTWgXaz3aewF+smo0rlnbgzeNa74TmgZgNvOaBZMoaEOv0PyOdTVH0Gm4ArPNU8oKM0Zxo2giNx+glvH7QCwXcyltpmjKJfOYE2eY5UxQFymDmtMIJS8ECZc43GYep1ZIlHUn843/tK10PolyZxH0QTK3VEOpOiKEdG6dODucxpzmGcIjGstmNrn9ASzqSeGJPLxa7NCzOaoygNRFGW4JDjpe1EFaL2QtYPrsRu3vc8yB9o2ajA9hzHMY7/bZk2FOR1w2rQB+gCwLa8xS6nlVrnMPxoZxrDsRKX6qAWYbZ45qcidnYFRyGMllj5GOW5j9ACyedGM7hhQNncPWyWVg1twlvFpvvjDImpfdOhHFk0N0D0TfikazgWtFnrHqzVivFisl4owdixSt/ZKWrOYpTI3nNa3DZWJvjbOn7zPGS4/S+ODde8iw63DTMbIiYqrncytirzZ0fuwDtDRx++pq/0vZ6QAwIwYY+A1ubv6BtAntOpXDhbK2qat2CFtdk3tLOBjz6eo9r/mNmY6Q0fZGitDthqzFYPqeplEgXJAV7LMak17LpAuOz4h3fsyFi64DPCRKilk3ZmmwXHeQq3EpoC5K2ybcmwsiLsmsOBDB7GVpH/fjnb3VQD3YOYUVMoRenHoE5LTEc6s+Al2Q0xcJY2JbAkTNZnBzOIcIythyGFsJy9kDCIRrfeeYAHtpxHIMZwVGB1kqal0qNmoDWkJgTZKQssXzjKjIF/yGs9qQ23tmoQmxlQVuilMPIOngggC5lP359WH9jq0xPEEl0nU3nzMBJjzxPvSEGhGCjo0Ert/3Wr97Gtfe/ik/871fxw5eOloQSv/rhxdi4qNXxtUs7G/DWyZSthFfnooUtuGHDPACaMTk6mLWFLFbNbcZLBwehKCr+e3cv/sfDb5me1/oszK9ZPsfda5nbYlcZ7hu1G6EEx5qa7ySLtLcXeo/BjOLGmnDYqHQaoizGih33Pak8upqNBsQ+ztQtid5v2PSsc7UBLR/xizd7sG6B9lvRNIVkJIQ7/msf/seHz7GtqzUexsmRvKMBuXHDfFy0oAW/3HVKK+H16CnSvzGrp6KPvk3lBDRGnV9fSQiLKeZPnIoddBYakuA5QXb0QNqTnOcoggVtMRwdMhgQh2R8rdAVEpxuIqYCU29FhLrT2RTF8aEc9veN4dGbN+CRz2/Ar774ASyZqYWlQgzteie+aEYCCS6EczqcpVbakxGsmqsJ481riTmGK+JcCBsWtuK37/TjZztP+BJ5XD67ybX816m0VAsdmT0fq2y52zwQpx6MvCAjyobQmuCQ4EKemkXGZsLeVB6zDR5Im4OIHi/KiFg2D2vy16pfBWif+7Wjw9h0zrhC8oo5zYiGGaws/gZGWhNhyIrqaECuWNqBS5e0Y3ZzFG8cH/FUVA6HtFnjQxYDQheHM6XyIprjzkbCOh+8HGyIwmCGdw1hLZhhNCDO/UvtDRHPirb5rXHTDYjmgQRjQJpjLEZyoqaQPMUS6AAxIAQHOpui+OWuHly0sLXiiZAsQ+NHN621Nb45MaclZirhNXLTB+bjriffwfI5Tdi6ohPPvatNs7TqGelc0NWIvadGMVYQbdVITpPnNC0tiwcSMavOus0DSUbsTYe6B9KaCJfdABsi470gp0asIaywTfyx4JBEZyzVS045kK6mKBoiISyfM96P8+mN83D3x8yjYnX0zd5ro7psSTsef/OUYxOhTlNMK3V2y5WM5Mxy8Ua8uvid+Pjq2finp/Zj+Wxnj3d+q9kDibFuISx3D2S+RSg062KIagFFaYUAWg/I1CrhBYgBITjQ1RzFr97qxZXnu0u/e7F2vr8BOUtmJrHERcqhNcHh5ksW4bMXL8Ali2fgxYNnAGgbf2eT3ejEuRAKooz7XziMvyqGyIxog4vGjUjfaMFUyQVoISxj74TTPBDAWc4kW+xybo6Fy1YxGZsJreswyqrr8A6NhICW/NVDYU69JBGWwSM3bzAVAjTFwiapdiNciEGSC3muf9NiLSbf5mFAtLtmAUNZwTYhE9DLUd2S6JUZkGtWzsaPblqHv/7gQsfn2wwikTnBOYne0WAOB1ppjodNv3dQVVg6kTCDvlSBeCCE6UFXUxSt8TCWdTnf1VWLZbOb8FWHWLzODevnobMpiqZYGKKsIMNLOJUqoLPReaDWgrY4Xjk8hKsumGV7Tq/Y0dFyIGZDlLRoPjnNAwG0SjJrSCxfvLtlaAr/fO1y188E6CEs7X0E2dyzYpRV1ymI2rx0K/Na4zhRDK04hbAAuGotudGa8DaALfEwVsxpss0CMdIUC2Mkq3kgTrkSa07HXMYr+s6B+MFYhutWBtvREEF/UWLHtQ3eUMqb5Z2T8bWiqymCd/rGiAdCmB7Mb43jOx9fFsjsAb8hsg0LW/HakSH0pZw9EAD42Iou/N9bljque15LDMcNHdJOIay4RcFWUuwlnQCwZn4LXjtqnulhFOq7oIzh1T0Qp+Y/o6y6TsEw38T0mQwjiKul1Lp1RZejt2Pkf//ValsXu5HmYrPlsIsHYsVYlZap0APxw6xGcxmuldZ4GINpHofPZLCo3d78CmhNsn3Fc7hpwdWKzsYo9vWOTjkZEyDAkbbZbBYPPfQQ9u/fj0Qiga1bt2Lt2rW245544gn85je/AcuO/zF885vfRFub+yhWQnUJh2h88JwZ9V6GifXFpHqEZbBhoXMF2Eavcb2tWk+EjlMYImGZZ2GV9tZZM68ZD7x4xPSYW3jEicYoi8NnMhjM8rZyWKusOqBJszh7ILGSEKBTDmQifPWKxWWPKTdSWZOOETHs4IGwDG37fO1JLQcxtzVWFKKs7rZ0fmcDdhwZchVp1Huf9hbHDzgxr1jI0dUUDUTKxEhnUxSPvdmDa9fMCew9/RLYt/Dwww+DYRjcc8896Onpwfbt29HV1YXOzk7bsatXr8ZNN90U1NII04Bls5vwnWcOYG5LrKRfVQnzWuN4co828ZCXZFuDIKDpVBmViAWLtLdOnAtBUhRTg1/eQSrcDV2RtzdVMJXwuqH3mFiZ2xLHb/f1A3APYdWDphiLU6m8owcSDTO2pk+tbLyAua0xjFVQxuuX69fNxV/9fztdu+d19p4adZxPDoxrYn2guw3pAKuwgPGqyKnogQQSwuJ5Hrt27cKWLVsQiUTQ3d2NZcuWYefOnUG8PeEsIByiEWUZHOxPlxR0K8HYC3J0MIuFM+yhCqvQoVMnus6KOc14y1A27DRrwg29jPfdvjEsclgHYBnKZBGH1JnTEi01mWUreP9a0xwLI5UTbI2EgBYmtCbQjX0YWb76HkgywmLL8k7PGfFJLoRdJ0ZKpepWjM2Ebh3ttUKvVJyKOZBAzOjAwABomkZHx/hksa6uLhw8eNDx+L179+LrX/86Ghsbcckll2DTpk1VWUc6nS5/EGHKsrwzjv/zpxSEfA7uSlnOhFQVg+k80uk09h4/gzkNrO16oGUew+lc6fFcgYdQyDteN8tnRfHi/j6cP0O7K0xlcuhM0K7XWCYzPjqVkUUMpfN45u08bt/cbXtNLAT0DaZKuQBRkpDNmkev6uR5Eel0Goosm96jnrCqgP7RHCRJsq0pRCkIsea/xSSr4uTgKNLpBARJQs7ls06Gj53fgigtu/4+zVEavSOy67XVxik41D+GY6eH0MC5/861IE4roACEFAHptPeYYidqeV0EYkB4nkc0anbVo9EoeN5ee7169WpcfPHFaGhowNGjR/HAAw8gGo065ksqJZl0vrsgTA8uOW8W/nBoZMK/I8eyCHExnByVsH5Rm+087S0UeGW49DjFhNCYTDi+3wfPi+L+l3fgC5cvQTLCQgKDtqak59r052JxFSMFGWGGxsJOe96mozEOngqjM6l5JwzDuJ43yrEIR2OgPY4JmtlsBFnhOEJMyLampngUbIg2PT6/Q8Sh4QEkEgkwdO0+x6c2uhc3dLUkwTCs63snk0BOVHEkJWH1Avu1U2s6m6LobGuquC9Lp1brDSSExXEc8nmzRHehUADH2UsBZ82ahaamJtA0jUWLFuGyyy7Drl27glgmYYqzbHYTvny5e9lvOTYuasUfDw3i4EAG57Tb/6CsZbzW8aZGElwIX71iMb72yG4oiuoqk+EEQ1M4PJApScNYcWomdGNOcwwvHDjj69igSHIhjOQEx+8uFmZsoRhd0+pMmseMhvIaW7VgQVsca+bbu/ONUBTw1on/v717D2rq2vcA/k2ChEdeEOSNoYKKeAT08NJWufR4HItU0WlFQMXaqvjota1Ty6gd9Z9eqqU6c8daa1vxAR11WkWxHi2OtrYgUrXirXjloTxEiWKRZ0JC1v2Daw6BBEI0D+D3memM2WvvtX+Lrskv+7HWakSIgUGL5rR1zgSTk4c5WSSBuLu7Q6PRQC6Xa7fV1tbqfYDeE4fDeWFrEJPBbQSPi3+M9+h/RwNmBHsgv7S+aw1yPQPhnPl2PV7jNfwMBAD+Y5w7/FydUFDR8P8P0Y2/oBc6jMCMYHe9ZVIBH4+7vcrb1/fGB/8ci4v/+0hnhUZr43A4XYsf6XmF14lv12sU+rNpWcoftSBgpP4pcMxtbpg33vi7b5/7eEsccfbPeoT4SPrczxz+GWx6vzcni12BhIWFIS8vD0qlEhUVFSgpKUFkZGSvfW/cuIG2tjYwxnDv3j1cuHABoaF9D8wixBhBnkKUPmzGCANzeTmN4PUYSMgwwsAkfc9E+Lvg9sMmtKkG9hA7LWY0gr30D/JzE+hOP9/X7yd3kQP+a/5E/HfSJKPPbQk8LkfvIMJwmYvO1CrAv6eAr5C3GHypwNw4HE6/v/D9pc5QqjsNjuQfjiz2LtrChQtx6NAhfPTRR3B2dkZSUhK8vb1RXl6O3bt3Y+fOnQCAq1ev4vDhw1Cr1ZBIJJg5cyaio6MtFSYZwjgcDib5SXSmNOmu5wBEQyPRuxvjIcSF24/QPoBxIACweIq/wbJna4oMZi5OI/S+dhrexzQ3FY9a+70KsCZ/NyeE+Fr+6sOWWSyBODs7Iy0trdf2wMBAbfIAgGXLllkqJDIMLYz007s64TPPfux3qDW419AGd2Hf9+S7Rri3gsvhwEnPWA1TeEsckPvHvxOIDd767pfEyd7golP62PO4uP2wSe/r1bbi5UA3jHI1PAJ/OLLcaBhCbECQpwgwYo7IH28+wKwJnnqnc+/OjseFRgNooH/qd1PIuk0fru7U9Fr4aTBwcRoxoATiLuTjek3jgJ4jWZqbgG/UQlrDCc2FRUg3HHQN4su5Uo2kSOOmjvAQO6C+j/UkBorH5YDD6bqFplRrjJoa39YM9Mt2pIhvcGp/YrtsN90TYgX2dlx8cPQGJo9yMXr67LHuAly996T/HQcg0L1r+Vk3gX2/kxvaov/8xxjYD+CKzF3oAOXIgQ+SI9ZFCYSQbpIiR8FL7KB3xT5DxngIX/jUFuO9RLj14CkiX5LqnUjR1umbu6svYX5i+Pcxwy+xTZRACOkmbmLvtUT6M9ZD8MLv3Qd7i/Cv/3mIEF+J3qnch5q/y4xbhIzYlqHfMwkxM5nUGbP+ZtrqjYYEeQpR+qAJ3xVVY4qBEeuEWBslEEKeE4/LwZrYwBdap5O9HRpaOnC/sR2vhwz8qogQS6BbWITYqOSoUZg5wcMm50AiBAA4bJhMNEVzahFram5utpnZcsnw8rx9r6/vTrqFRQghxCSUQAghhJiEEgghhBCTUAIhhBBiEkoghBBCTEIJhBBCiEkogRBCCDEJJRBCCCEmGVYj0WlELyGEvDjDJoHQKHRCCHmx6BYWIYQQk1ACIYQQYhJKIIQQQkxCCYQQQohJKIEQQggxCSUQQgghJhk2r/Eao6GhAZ9++im8vLqWEH3nnXdoESBiUcXFxTh27Bi2b99u7VDIMNHU1IS9e/eCx+OBy+XirbfeglgsNupYSiA9jBkzBsuXL7d2GGQY0mg0uHbtGlxcXKwdChlGBAIB1q9fDy6Xi8LCQhQUFOC1114z6li6hdVDRUUFMjMzkZubS4MPiUUVFxdj8uTJNGMCsSgulwsutysVKBQK7R0YYwzaK5CLFy/i8uXLqKurQ3h4OJYsWaIta21txeHDh1FaWgqBQIC5c+ciIiKi3zpFIhG2bdsGe3t7ZGdn448//sCkSZPM2QwyCJmj7z27+li5ciXOnz9vzvDJIGaOvgcANTU1+O6779DW1oZ3333X6HgGbQIRi8WYNWsWSktLoVKpdMqOHDkCHo+HjIwM1NbW4osvvoCPjw+8vb3x9OlTfPvtt73qW7Zsmc59v7CwMNy9e5cSCOnFHH2vtLQUkydP1v4SJEQfc33v+fn5YcOGDbh69SrOnj2L5ORko+IZtAnk2Rd7dXU1GhsbtduVSiWuX7+OzZs3w8HBAYGBgQgJCcGVK1eQkJAAsViM999/X2+dCoUCDg4OAIDy8nJ4enqavyFk0DFH33vw4AFqampQXFwMuVyOo0ePYsGCBRZpDxk8zNH31Go17Oy6UoGjoyPs7e2NjmfQJhBD5HI5uFwuPDw8tNt8fHxQVlbW77EVFRU4efIk7O3tIZVK8frrr5szVDLEPE/fmzdvnvbfGRkZlDzIgDxP36utrcUPP/wADoeDESNGYPHixUafd8glEKVSCUdHR51tjo6OUCqV/R47YcIETJgwwVyhkSHuefped+np6S8yLDIMPE/f8/f3xwcffGDSeYfcDVc+n4/29nadbQqFAnw+30oRkeGC+h6xFmv1vSGXQNzd3aHRaCCXy7Xbamtr4e3tbcWoyHBAfY9Yi7X63qBNIJ2dnVCpVNBoNNBoNFCpVOjs7ASfz0dYWBjy8vKgVCpRUVGBkpISREZGWjtkMkRQ3yPWYmt9j8MG6Wi5vLw8/Pjjjzrb4uLiEB8fj9bWVhw6dAi3b9+Gs7MzEhISjH4fmpD+UN8j1mJrfW/QJhBCCCHWNWhvYRFCCLEuSiCEEEJMQgmEEEKISSiBEEIIMQklEEIIISahBEIIIcQklEAIIYSYhBIIsQh/f3/k5+db5dwCgQCVlZVWObc5JCUl4cSJEwCArKwsvPLKK1aOyDjG9oH6+nqMHz9+wJNQEsujBEKGvJaWFowePdraYQAAOBwOysvLTT6+pKQEN27cwNy5c19gVLbFw8MDsbGx+Oqrr6wdCukHJRAyqHV2dlo7BC21Wm32c+zduxcpKSlDft30lJQU7N2719phkH5QAiEWp9FokJGRgYCAAEilUixYsABPnjzRlr/55pvw9PSEWCzG9OnT8eeff2rLli5dilWrViEuLg7Ozs64cOECli5dijVr1mD27NkQCoWIiopCRUWF9pjuv/r72/fcuXMYN24cxGIxVq9ejZiYGHz99dd627F161a88cYbWLRoEUQiEbKysnDlyhVMmTIFEokEXl5eWLt2LTo6OgAA06dPBwCEhoZCIBDgyJEjALrmNwoLC4NEIsHUqVNRUlJi8G935swZxMTEGCwvKChAREQExGIxIiIiUFBQoC27e/cupk+fDqFQiBkzZmDNmjVYtGiR3noeP36M+Ph4SCQSuLq6Ytq0adBoNAC61s+eP38+Ro4cCalUirVr1wLoWpDt1VdfhVQqhZubG1JSUnRWzeuuvz4QFRWFyspKVFVVGWwrsQGMEAuQyWTsp59+YowxtmvXLhYVFcVqamqYQqFgK1asYAsXLtTu+80337CmpiamUCjYunXrWGhoqLYsNTWViUQi9uuvv7LOzk7W3t7OUlNTmaurKysqKmIqlYolJyezxMRE7TEAWFlZmfZ4Q/s+evSICYVC9v333zOVSsV27drF7Ozs2L59+/S2acuWLczOzo4dP36cdXZ2sra2Nvb777+zwsJCplKp2N27d1lQUBDbuXOn3lgYY+zatWts5MiR7PLly0ytVrOsrCwmk8mYQqHodb6WlhYGgMnlcu22/fv3s5dffpkxxlhDQwOTSCTs4MGDTKVSsZycHCaRSNjjx48ZY4xFR0ez9evXM6VSyS5dusSEQiFLSUnR27b09HS2cuVK1tHRwTo6Otgvv/zCNBoNU6vVLCQkhL333nuspaWFtbe3s0uXLjHGGCsrK2Pnzp1jCoWCyeVyNm3aNLZu3TqT+gBjjE2cOJHl5ubqjY/YBkogxCK6f3kEBQWx/Px8bVldXR2zs7NjKpWq13F//fUXA8AaGxsZY10JYPHixTr7pKamsrffflv7+fTp02zcuHHazz0TiKF9Dxw4wKKjo7VlGo2G+fr69plApk2b1me7d+7cyRISEvTGwhhjaWlpbPPmzTrHjB07ll28eLFXXbW1tQwAa29v127rnkAOHjzIIiIidI6Jjo5m+/fvZ1VVVYzH47HW1lZtWUpKisEE8vHHH7M5c+boxMoYYwUFBczNzU3v/6uejh8/zsLCwrSfB9oHpk6dyg4cONDveYj10C0sYnFVVVWYN28eJBIJJBIJxo8fDx6Ph/r6enR2diI9PR0BAQEQiUTw9/cH0HVL5Rk/P79edXp6emr/7eTkhJaWFoPnN7RvXV2dTt0cDge+vr59tqVnLHfu3EF8fDw8PT0hEomwceNGndh7qqqqQmZmpvZvIZFIUFNTg7q6ul77SiQSAEBzc7Peuurq6iCTyXS2yWQy3L9/H3V1dXB1dYWTk5PB2Lv78MMPERgYiJkzZ2L06NHIyMgA0HX7SiaTwc6u92rY9fX1WLhwIXx8fCASibBo0SKDbe+rDzzT3NysbTOxTZRAiMX5+fnhzJkzaGxs1P6nUCjg4+ODnJwc5ObmIj8/H0+fPsW9e/cAAKzbqgPmeoDs5eWF2tpa7WfGmM5nfXrGsmrVKgQFBaGsrAxNTU345JNPdGLvyc/PD5s2bdL5W7S1tSEpKanXvs7OzggICMCdO3f01uXt7d3rmUF1dTV8fHzg5eWFJ0+eoK2tTVtWU1NjMC6hUIjMzExUVlbi5MmT+Pzzz3H+/Hn4+fmhurpa7wsDGzduBIfDwc2bN9HU1ITDhw8bbHtffQDoeiGhvLwcoaGhBmMk1kcJhFhcWloaNm3apP2ye/ToEXJzcwF0OhJFlgAAAsBJREFU/erk8/mQSqVoa2vDxo0bLRbX7NmzcfPmTZw4cQJqtRq7d+/Gw4cPB1RHc3MzRCIRBAIBbt++jT179uiUe3h46IxJWb58Ob788ksUFRWBMYbW1lacPn3a4FVGXFwcfv75Z4Nld+7cQU5ODtRqNY4cOYJbt24hPj4eMpkM4eHh2Lp1Kzo6OlBYWIhTp04ZbEdeXh7Ky8vBGINYLAaPxwOXy0VkZCS8vLyQnp6O1tZWKBQK/Pbbb9q2CwQCiMVi3L9/Hzt27DBYf199AACuXLkCf3//XldUxLZQAiEWt27dOsyZMwczZ86EUChEdHQ0ioqKAABLliyBTCaDj48PgoODER0dbbG43NzccOzYMWzYsAFSqRS3bt1CeHg4+Hy+0XV89tlnyMnJgVAoxPLly5GYmKhTvnXrVqSmpkIikeDo0aMIDw/Hvn37sHbtWri4uCAwMBBZWVkG61+xYgWys7P1/rKXSqXIy8tDZmYmpFIptm/fjry8PLi5uQEAsrOzUVhYCKlUis2bNyMxMdFg28rKyjBjxgwIBAJMmTIFq1evRmxsLHg8Hk6dOoXy8nKMGjUKvr6+2rfJtmzZgmvXrkEsFmP27NmYP3++wXb01QeexZqWlmbweGIbaEVCQgzQaDTw9fVFdnY2YmNjrR2OVnJyMhYsWICEhITnqicxMRFBQUHYtm3bC4rsxZDL5YiJicH169fh4OBg7XBIHyiBENLN2bNnERUVBUdHR+zYsQO7d+9GZWUlHB0drR3acysuLoarqyteeuklnDt3DgkJCSgsLMSkSZOsHRoZpHq/SkHIMFZYWIjk5GR0dHQgODgYJ06cGBLJAwAePnyI+fPno6GhAb6+vtizZw8lD/Jc6AqEEEKISeghOiGEEJNQAiGEEGISSiCEEEJMQgmEEEKISSiBEEIIMQklEEIIISb5P9w0a9080QEtAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "lr_finder.plot_loss()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Hyper parameter experiment to fix batch size mainly"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "metadata": {},
   "outputs": [],
   "source": [
    "params = {\n",
    "    'bert_trainable' :[False],\n",
    "    'text_no_hidden_layer':[1],\n",
    "    'text_hidden_neurons':[768],\n",
    "    'dropout':[0.4],\n",
    "    'repr_size':[32],\n",
    "    'vis_no_hidden_layer':[1],\n",
    "    'vis_hidden_neurons':[2742],\n",
    "    'final_no_hidden_layer':[1],\n",
    "    'final_hidden_neurons':[35],\n",
    "    'optimizer':[tf.keras.optimizers.Adam],\n",
    "    'batch_size':[128,256,512],\n",
    "    'epochs':[10],\n",
    "    'lr':[0.00005,0.0005,0.00075]\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 200,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "\n",
      "\n",
      "100%|██████████| 9/9 [1:05:08<00:00, 433.76s/it]\u001b[A\u001b[A\u001b[A\u001b[A"
     ]
    }
   ],
   "source": [
    "h = ta.Scan([train_input_ids, train_input_masks, train_segment_ids,train_imagesX], trainY, params=params,\n",
    "            model=news_model,\n",
    "            dataset_name='twitter_fake_news',\n",
    "            experiment_no='3',\n",
    "            x_val=[test_input_ids, test_input_masks, test_segment_ids,test_imagesX],\n",
    "            y_val=testY)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "metadata": {},
   "outputs": [],
   "source": [
    "r = ta.Reporting(h)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 202,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7480769230769231"
      ]
     },
     "execution_count": 202,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r.high('val_acc')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 204,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>round_epochs</th>\n",
       "      <th>val_loss</th>\n",
       "      <th>val_acc</th>\n",
       "      <th>loss</th>\n",
       "      <th>acc</th>\n",
       "      <th>bert_trainable</th>\n",
       "      <th>text_no_hidden_layer</th>\n",
       "      <th>text_hidden_neurons</th>\n",
       "      <th>dropout</th>\n",
       "      <th>repr_size</th>\n",
       "      <th>vis_no_hidden_layer</th>\n",
       "      <th>vis_hidden_neurons</th>\n",
       "      <th>final_no_hidden_layer</th>\n",
       "      <th>final_hidden_neurons</th>\n",
       "      <th>optimizer</th>\n",
       "      <th>batch_size</th>\n",
       "      <th>epochs</th>\n",
       "      <th>lr</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10</td>\n",
       "      <td>0.684112</td>\n",
       "      <td>0.567308</td>\n",
       "      <td>0.585147</td>\n",
       "      <td>0.568360</td>\n",
       "      <td>False</td>\n",
       "      <td>1</td>\n",
       "      <td>768</td>\n",
       "      <td>0.4</td>\n",
       "      <td>32</td>\n",
       "      <td>1</td>\n",
       "      <td>2742</td>\n",
       "      <td>1</td>\n",
       "      <td>35</td>\n",
       "      <td>&lt;class 'tensorflow.python.keras.optimizers.Adam'&gt;</td>\n",
       "      <td>256</td>\n",
       "      <td>10</td>\n",
       "      <td>0.00075</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>10</td>\n",
       "      <td>0.690176</td>\n",
       "      <td>0.616346</td>\n",
       "      <td>0.288019</td>\n",
       "      <td>0.892743</td>\n",
       "      <td>False</td>\n",
       "      <td>1</td>\n",
       "      <td>768</td>\n",
       "      <td>0.4</td>\n",
       "      <td>32</td>\n",
       "      <td>1</td>\n",
       "      <td>2742</td>\n",
       "      <td>1</td>\n",
       "      <td>35</td>\n",
       "      <td>&lt;class 'tensorflow.python.keras.optimizers.Adam'&gt;</td>\n",
       "      <td>512</td>\n",
       "      <td>10</td>\n",
       "      <td>0.00075</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>10</td>\n",
       "      <td>0.757478</td>\n",
       "      <td>0.671154</td>\n",
       "      <td>0.091138</td>\n",
       "      <td>0.975386</td>\n",
       "      <td>False</td>\n",
       "      <td>1</td>\n",
       "      <td>768</td>\n",
       "      <td>0.4</td>\n",
       "      <td>32</td>\n",
       "      <td>1</td>\n",
       "      <td>2742</td>\n",
       "      <td>1</td>\n",
       "      <td>35</td>\n",
       "      <td>&lt;class 'tensorflow.python.keras.optimizers.Adam'&gt;</td>\n",
       "      <td>128</td>\n",
       "      <td>10</td>\n",
       "      <td>0.00005</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>10</td>\n",
       "      <td>0.693186</td>\n",
       "      <td>0.689423</td>\n",
       "      <td>0.211171</td>\n",
       "      <td>0.926680</td>\n",
       "      <td>False</td>\n",
       "      <td>1</td>\n",
       "      <td>768</td>\n",
       "      <td>0.4</td>\n",
       "      <td>32</td>\n",
       "      <td>1</td>\n",
       "      <td>2742</td>\n",
       "      <td>1</td>\n",
       "      <td>35</td>\n",
       "      <td>&lt;class 'tensorflow.python.keras.optimizers.Adam'&gt;</td>\n",
       "      <td>512</td>\n",
       "      <td>10</td>\n",
       "      <td>0.00005</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>10</td>\n",
       "      <td>0.662417</td>\n",
       "      <td>0.704808</td>\n",
       "      <td>0.127978</td>\n",
       "      <td>0.957858</td>\n",
       "      <td>False</td>\n",
       "      <td>1</td>\n",
       "      <td>768</td>\n",
       "      <td>0.4</td>\n",
       "      <td>32</td>\n",
       "      <td>1</td>\n",
       "      <td>2742</td>\n",
       "      <td>1</td>\n",
       "      <td>35</td>\n",
       "      <td>&lt;class 'tensorflow.python.keras.optimizers.Adam'&gt;</td>\n",
       "      <td>512</td>\n",
       "      <td>10</td>\n",
       "      <td>0.00050</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10</td>\n",
       "      <td>0.695243</td>\n",
       "      <td>0.713462</td>\n",
       "      <td>0.148110</td>\n",
       "      <td>0.947341</td>\n",
       "      <td>False</td>\n",
       "      <td>1</td>\n",
       "      <td>768</td>\n",
       "      <td>0.4</td>\n",
       "      <td>32</td>\n",
       "      <td>1</td>\n",
       "      <td>2742</td>\n",
       "      <td>1</td>\n",
       "      <td>35</td>\n",
       "      <td>&lt;class 'tensorflow.python.keras.optimizers.Adam'&gt;</td>\n",
       "      <td>128</td>\n",
       "      <td>10</td>\n",
       "      <td>0.00075</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>10</td>\n",
       "      <td>0.722816</td>\n",
       "      <td>0.730769</td>\n",
       "      <td>0.116719</td>\n",
       "      <td>0.966659</td>\n",
       "      <td>False</td>\n",
       "      <td>1</td>\n",
       "      <td>768</td>\n",
       "      <td>0.4</td>\n",
       "      <td>32</td>\n",
       "      <td>1</td>\n",
       "      <td>2742</td>\n",
       "      <td>1</td>\n",
       "      <td>35</td>\n",
       "      <td>&lt;class 'tensorflow.python.keras.optimizers.Adam'&gt;</td>\n",
       "      <td>256</td>\n",
       "      <td>10</td>\n",
       "      <td>0.00005</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>10</td>\n",
       "      <td>0.730194</td>\n",
       "      <td>0.743269</td>\n",
       "      <td>0.048878</td>\n",
       "      <td>0.986947</td>\n",
       "      <td>False</td>\n",
       "      <td>1</td>\n",
       "      <td>768</td>\n",
       "      <td>0.4</td>\n",
       "      <td>32</td>\n",
       "      <td>1</td>\n",
       "      <td>2742</td>\n",
       "      <td>1</td>\n",
       "      <td>35</td>\n",
       "      <td>&lt;class 'tensorflow.python.keras.optimizers.Adam'&gt;</td>\n",
       "      <td>128</td>\n",
       "      <td>10</td>\n",
       "      <td>0.00050</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>10</td>\n",
       "      <td>0.702392</td>\n",
       "      <td>0.748077</td>\n",
       "      <td>0.322046</td>\n",
       "      <td>0.879690</td>\n",
       "      <td>False</td>\n",
       "      <td>1</td>\n",
       "      <td>768</td>\n",
       "      <td>0.4</td>\n",
       "      <td>32</td>\n",
       "      <td>1</td>\n",
       "      <td>2742</td>\n",
       "      <td>1</td>\n",
       "      <td>35</td>\n",
       "      <td>&lt;class 'tensorflow.python.keras.optimizers.Adam'&gt;</td>\n",
       "      <td>256</td>\n",
       "      <td>10</td>\n",
       "      <td>0.00050</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   round_epochs  val_loss   val_acc      loss       acc bert_trainable  \\\n",
       "0            10  0.684112  0.567308  0.585147  0.568360          False   \n",
       "5            10  0.690176  0.616346  0.288019  0.892743          False   \n",
       "4            10  0.757478  0.671154  0.091138  0.975386          False   \n",
       "7            10  0.693186  0.689423  0.211171  0.926680          False   \n",
       "6            10  0.662417  0.704808  0.127978  0.957858          False   \n",
       "1            10  0.695243  0.713462  0.148110  0.947341          False   \n",
       "3            10  0.722816  0.730769  0.116719  0.966659          False   \n",
       "8            10  0.730194  0.743269  0.048878  0.986947          False   \n",
       "2            10  0.702392  0.748077  0.322046  0.879690          False   \n",
       "\n",
       "   text_no_hidden_layer  text_hidden_neurons  dropout  repr_size  \\\n",
       "0                     1                  768      0.4         32   \n",
       "5                     1                  768      0.4         32   \n",
       "4                     1                  768      0.4         32   \n",
       "7                     1                  768      0.4         32   \n",
       "6                     1                  768      0.4         32   \n",
       "1                     1                  768      0.4         32   \n",
       "3                     1                  768      0.4         32   \n",
       "8                     1                  768      0.4         32   \n",
       "2                     1                  768      0.4         32   \n",
       "\n",
       "   vis_no_hidden_layer  vis_hidden_neurons  final_no_hidden_layer  \\\n",
       "0                    1                2742                      1   \n",
       "5                    1                2742                      1   \n",
       "4                    1                2742                      1   \n",
       "7                    1                2742                      1   \n",
       "6                    1                2742                      1   \n",
       "1                    1                2742                      1   \n",
       "3                    1                2742                      1   \n",
       "8                    1                2742                      1   \n",
       "2                    1                2742                      1   \n",
       "\n",
       "   final_hidden_neurons                                          optimizer  \\\n",
       "0                    35  <class 'tensorflow.python.keras.optimizers.Adam'>   \n",
       "5                    35  <class 'tensorflow.python.keras.optimizers.Adam'>   \n",
       "4                    35  <class 'tensorflow.python.keras.optimizers.Adam'>   \n",
       "7                    35  <class 'tensorflow.python.keras.optimizers.Adam'>   \n",
       "6                    35  <class 'tensorflow.python.keras.optimizers.Adam'>   \n",
       "1                    35  <class 'tensorflow.python.keras.optimizers.Adam'>   \n",
       "3                    35  <class 'tensorflow.python.keras.optimizers.Adam'>   \n",
       "8                    35  <class 'tensorflow.python.keras.optimizers.Adam'>   \n",
       "2                    35  <class 'tensorflow.python.keras.optimizers.Adam'>   \n",
       "\n",
       "   batch_size  epochs       lr  \n",
       "0         256      10  0.00075  \n",
       "5         512      10  0.00075  \n",
       "4         128      10  0.00005  \n",
       "7         512      10  0.00005  \n",
       "6         512      10  0.00050  \n",
       "1         128      10  0.00075  \n",
       "3         256      10  0.00005  \n",
       "8         128      10  0.00050  \n",
       "2         256      10  0.00050  "
      ]
     },
     "execution_count": 204,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r.data.sort_values('val_acc')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 205,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 792x792 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "r.plot_corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 206,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "batch_size\n",
      "lr\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x475.2 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x475.2 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for k in ['batch_size','lr']:\n",
    "    print(k)\n",
    "    r.plot_box(y='val_acc',x=k)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lets just try to train best two models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 207,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>round_epochs</th>\n",
       "      <th>val_loss</th>\n",
       "      <th>val_acc</th>\n",
       "      <th>loss</th>\n",
       "      <th>acc</th>\n",
       "      <th>bert_trainable</th>\n",
       "      <th>text_no_hidden_layer</th>\n",
       "      <th>text_hidden_neurons</th>\n",
       "      <th>dropout</th>\n",
       "      <th>repr_size</th>\n",
       "      <th>vis_no_hidden_layer</th>\n",
       "      <th>vis_hidden_neurons</th>\n",
       "      <th>final_no_hidden_layer</th>\n",
       "      <th>final_hidden_neurons</th>\n",
       "      <th>optimizer</th>\n",
       "      <th>batch_size</th>\n",
       "      <th>epochs</th>\n",
       "      <th>lr</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10</td>\n",
       "      <td>0.684112</td>\n",
       "      <td>0.567308</td>\n",
       "      <td>0.585147</td>\n",
       "      <td>0.568360</td>\n",
       "      <td>False</td>\n",
       "      <td>1</td>\n",
       "      <td>768</td>\n",
       "      <td>0.4</td>\n",
       "      <td>32</td>\n",
       "      <td>1</td>\n",
       "      <td>2742</td>\n",
       "      <td>1</td>\n",
       "      <td>35</td>\n",
       "      <td>&lt;class 'tensorflow.python.keras.optimizers.Adam'&gt;</td>\n",
       "      <td>256</td>\n",
       "      <td>10</td>\n",
       "      <td>0.00075</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>10</td>\n",
       "      <td>0.690176</td>\n",
       "      <td>0.616346</td>\n",
       "      <td>0.288019</td>\n",
       "      <td>0.892743</td>\n",
       "      <td>False</td>\n",
       "      <td>1</td>\n",
       "      <td>768</td>\n",
       "      <td>0.4</td>\n",
       "      <td>32</td>\n",
       "      <td>1</td>\n",
       "      <td>2742</td>\n",
       "      <td>1</td>\n",
       "      <td>35</td>\n",
       "      <td>&lt;class 'tensorflow.python.keras.optimizers.Adam'&gt;</td>\n",
       "      <td>512</td>\n",
       "      <td>10</td>\n",
       "      <td>0.00075</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>10</td>\n",
       "      <td>0.757478</td>\n",
       "      <td>0.671154</td>\n",
       "      <td>0.091138</td>\n",
       "      <td>0.975386</td>\n",
       "      <td>False</td>\n",
       "      <td>1</td>\n",
       "      <td>768</td>\n",
       "      <td>0.4</td>\n",
       "      <td>32</td>\n",
       "      <td>1</td>\n",
       "      <td>2742</td>\n",
       "      <td>1</td>\n",
       "      <td>35</td>\n",
       "      <td>&lt;class 'tensorflow.python.keras.optimizers.Adam'&gt;</td>\n",
       "      <td>128</td>\n",
       "      <td>10</td>\n",
       "      <td>0.00005</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>10</td>\n",
       "      <td>0.693186</td>\n",
       "      <td>0.689423</td>\n",
       "      <td>0.211171</td>\n",
       "      <td>0.926680</td>\n",
       "      <td>False</td>\n",
       "      <td>1</td>\n",
       "      <td>768</td>\n",
       "      <td>0.4</td>\n",
       "      <td>32</td>\n",
       "      <td>1</td>\n",
       "      <td>2742</td>\n",
       "      <td>1</td>\n",
       "      <td>35</td>\n",
       "      <td>&lt;class 'tensorflow.python.keras.optimizers.Adam'&gt;</td>\n",
       "      <td>512</td>\n",
       "      <td>10</td>\n",
       "      <td>0.00005</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>10</td>\n",
       "      <td>0.662417</td>\n",
       "      <td>0.704808</td>\n",
       "      <td>0.127978</td>\n",
       "      <td>0.957858</td>\n",
       "      <td>False</td>\n",
       "      <td>1</td>\n",
       "      <td>768</td>\n",
       "      <td>0.4</td>\n",
       "      <td>32</td>\n",
       "      <td>1</td>\n",
       "      <td>2742</td>\n",
       "      <td>1</td>\n",
       "      <td>35</td>\n",
       "      <td>&lt;class 'tensorflow.python.keras.optimizers.Adam'&gt;</td>\n",
       "      <td>512</td>\n",
       "      <td>10</td>\n",
       "      <td>0.00050</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10</td>\n",
       "      <td>0.695243</td>\n",
       "      <td>0.713462</td>\n",
       "      <td>0.148110</td>\n",
       "      <td>0.947341</td>\n",
       "      <td>False</td>\n",
       "      <td>1</td>\n",
       "      <td>768</td>\n",
       "      <td>0.4</td>\n",
       "      <td>32</td>\n",
       "      <td>1</td>\n",
       "      <td>2742</td>\n",
       "      <td>1</td>\n",
       "      <td>35</td>\n",
       "      <td>&lt;class 'tensorflow.python.keras.optimizers.Adam'&gt;</td>\n",
       "      <td>128</td>\n",
       "      <td>10</td>\n",
       "      <td>0.00075</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>10</td>\n",
       "      <td>0.722816</td>\n",
       "      <td>0.730769</td>\n",
       "      <td>0.116719</td>\n",
       "      <td>0.966659</td>\n",
       "      <td>False</td>\n",
       "      <td>1</td>\n",
       "      <td>768</td>\n",
       "      <td>0.4</td>\n",
       "      <td>32</td>\n",
       "      <td>1</td>\n",
       "      <td>2742</td>\n",
       "      <td>1</td>\n",
       "      <td>35</td>\n",
       "      <td>&lt;class 'tensorflow.python.keras.optimizers.Adam'&gt;</td>\n",
       "      <td>256</td>\n",
       "      <td>10</td>\n",
       "      <td>0.00005</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>10</td>\n",
       "      <td>0.730194</td>\n",
       "      <td>0.743269</td>\n",
       "      <td>0.048878</td>\n",
       "      <td>0.986947</td>\n",
       "      <td>False</td>\n",
       "      <td>1</td>\n",
       "      <td>768</td>\n",
       "      <td>0.4</td>\n",
       "      <td>32</td>\n",
       "      <td>1</td>\n",
       "      <td>2742</td>\n",
       "      <td>1</td>\n",
       "      <td>35</td>\n",
       "      <td>&lt;class 'tensorflow.python.keras.optimizers.Adam'&gt;</td>\n",
       "      <td>128</td>\n",
       "      <td>10</td>\n",
       "      <td>0.00050</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>10</td>\n",
       "      <td>0.702392</td>\n",
       "      <td>0.748077</td>\n",
       "      <td>0.322046</td>\n",
       "      <td>0.879690</td>\n",
       "      <td>False</td>\n",
       "      <td>1</td>\n",
       "      <td>768</td>\n",
       "      <td>0.4</td>\n",
       "      <td>32</td>\n",
       "      <td>1</td>\n",
       "      <td>2742</td>\n",
       "      <td>1</td>\n",
       "      <td>35</td>\n",
       "      <td>&lt;class 'tensorflow.python.keras.optimizers.Adam'&gt;</td>\n",
       "      <td>256</td>\n",
       "      <td>10</td>\n",
       "      <td>0.00050</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   round_epochs  val_loss   val_acc      loss       acc bert_trainable  \\\n",
       "0            10  0.684112  0.567308  0.585147  0.568360          False   \n",
       "5            10  0.690176  0.616346  0.288019  0.892743          False   \n",
       "4            10  0.757478  0.671154  0.091138  0.975386          False   \n",
       "7            10  0.693186  0.689423  0.211171  0.926680          False   \n",
       "6            10  0.662417  0.704808  0.127978  0.957858          False   \n",
       "1            10  0.695243  0.713462  0.148110  0.947341          False   \n",
       "3            10  0.722816  0.730769  0.116719  0.966659          False   \n",
       "8            10  0.730194  0.743269  0.048878  0.986947          False   \n",
       "2            10  0.702392  0.748077  0.322046  0.879690          False   \n",
       "\n",
       "   text_no_hidden_layer  text_hidden_neurons  dropout  repr_size  \\\n",
       "0                     1                  768      0.4         32   \n",
       "5                     1                  768      0.4         32   \n",
       "4                     1                  768      0.4         32   \n",
       "7                     1                  768      0.4         32   \n",
       "6                     1                  768      0.4         32   \n",
       "1                     1                  768      0.4         32   \n",
       "3                     1                  768      0.4         32   \n",
       "8                     1                  768      0.4         32   \n",
       "2                     1                  768      0.4         32   \n",
       "\n",
       "   vis_no_hidden_layer  vis_hidden_neurons  final_no_hidden_layer  \\\n",
       "0                    1                2742                      1   \n",
       "5                    1                2742                      1   \n",
       "4                    1                2742                      1   \n",
       "7                    1                2742                      1   \n",
       "6                    1                2742                      1   \n",
       "1                    1                2742                      1   \n",
       "3                    1                2742                      1   \n",
       "8                    1                2742                      1   \n",
       "2                    1                2742                      1   \n",
       "\n",
       "   final_hidden_neurons                                          optimizer  \\\n",
       "0                    35  <class 'tensorflow.python.keras.optimizers.Adam'>   \n",
       "5                    35  <class 'tensorflow.python.keras.optimizers.Adam'>   \n",
       "4                    35  <class 'tensorflow.python.keras.optimizers.Adam'>   \n",
       "7                    35  <class 'tensorflow.python.keras.optimizers.Adam'>   \n",
       "6                    35  <class 'tensorflow.python.keras.optimizers.Adam'>   \n",
       "1                    35  <class 'tensorflow.python.keras.optimizers.Adam'>   \n",
       "3                    35  <class 'tensorflow.python.keras.optimizers.Adam'>   \n",
       "8                    35  <class 'tensorflow.python.keras.optimizers.Adam'>   \n",
       "2                    35  <class 'tensorflow.python.keras.optimizers.Adam'>   \n",
       "\n",
       "   batch_size  epochs       lr  \n",
       "0         256      10  0.00075  \n",
       "5         512      10  0.00075  \n",
       "4         128      10  0.00005  \n",
       "7         512      10  0.00005  \n",
       "6         512      10  0.00050  \n",
       "1         128      10  0.00075  \n",
       "3         256      10  0.00005  \n",
       "8         128      10  0.00050  \n",
       "2         256      10  0.00050  "
      ]
     },
     "execution_count": 207,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r.data.sort_values('val_acc')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 238,
   "metadata": {},
   "outputs": [],
   "source": [
    "params_final = {\n",
    "    'bert_trainable' :False,\n",
    "    'text_no_hidden_layer':1,\n",
    "    'text_hidden_neurons':768,\n",
    "    'dropout':0.4,\n",
    "    'repr_size':32,\n",
    "    'vis_no_hidden_layer':1,\n",
    "    'vis_hidden_neurons':2742,\n",
    "    'final_no_hidden_layer':1,\n",
    "    'final_hidden_neurons':35,\n",
    "    'optimizer':tf.keras.optimizers.Adam\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 239,
   "metadata": {},
   "outputs": [],
   "source": [
    "model=get_news_model(params_final)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 240,
   "metadata": {},
   "outputs": [],
   "source": [
    "K.set_value(model.optimizer.lr, 0.0005)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 241,
   "metadata": {},
   "outputs": [],
   "source": [
    "checkpoint = tf.keras.callbacks.ModelCheckpoint('model-{epoch:03d}-{val_acc:03f}.h5', verbose=1, monitor='val_acc',save_best_only=True, mode='max')  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 242,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Epoch 00020: val_acc did not improve from 0.77692\n"
     ]
    }
   ],
   "source": [
    "out = model.fit([train_input_ids, train_input_masks, train_segment_ids,train_imagesX], trainY,\n",
    "                    batch_size=256,\n",
    "                    epochs=20,\n",
    "                    verbose=0,\n",
    "                    shuffle=True,\n",
    "                    validation_data=([test_input_ids, test_input_masks, test_segment_ids,test_imagesX],testY),callbacks=[live(),checkpoint])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 243,
   "metadata": {},
   "outputs": [],
   "source": [
    "model=get_news_model(params_final)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 244,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.load_weights('model-010-0.776923.h5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 245,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_predict = model.predict([test_input_ids, test_input_masks, test_segment_ids,test_imagesX])\n",
    "test_predict = [1 if i>=0.5 else 0 for i in test_predict]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 246,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import accuracy_score,f1_score,precision_score,recall_score\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 247,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy == 0.7769230769230769\n",
      "F1 == [0.8218126  0.70179949]\n",
      "Precision == [0.75140449 0.83231707]\n",
      "Recall == [0.90677966 0.60666667]\n"
     ]
    }
   ],
   "source": [
    "print(f'Accuracy == {accuracy_score(testY,test_predict)}')\n",
    "print(f'F1 == {f1_score(testY,test_predict,average=None)}')\n",
    "print(f'Precision == {precision_score(testY,test_predict,average=None)}')\n",
    "print(f'Recall == {recall_score(testY,test_predict,average=None)}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {},
   "outputs": [],
   "source": [
    "# results = dict()\n",
    "# for i in tqdm(range(20)):\n",
    "#     p = dict()\n",
    "#     for k,v in params.items():\n",
    "#         p[k] = choice(v)\n",
    "        \n",
    "#     history,model = news_model([train_input_ids, train_input_masks, train_segment_ids,train_imagesX], trainY, [test_input_ids, test_input_masks, test_segment_ids,test_imagesX],testY,p)\n",
    "#     val_loss, val_acc = model.evaluate([test_input_ids, test_input_masks, test_segment_ids,test_imagesX],testY,verbose=0)\n",
    "# #     train_loss,train_acc = model.evaluate([train_input_ids, train_input_masks, train_segment_ids,train_imagesX],trainY)\n",
    "  \n",
    "#     results[i] = {'p':p,'val_loss':val_loss,'val_acc':val_acc,'history':history}\n",
    "# start=0.001\n",
    "# end=0.01\n",
    "# n=4\n",
    "# np.arange(start, end, (end - start) / n, dtype=float)/100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "# K.clear_session()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "# lr_finder = LRFinder(model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "# lr_finder.find([train_input_ids, train_input_masks, train_segment_ids,train_imagesX], trainY, 0.0000001, 1, 512, 8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "# lr_finder.plot_loss()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "# lr_finder.plot_loss_change(sma=20, n_skip_beginning=20, n_skip_end=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "# K.set_value(model.optimizer.lr, 0.00005)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "# history = model.fit([train_input_ids, train_input_masks, train_segment_ids,train_imagesX], trainY, validation_data=([test_input_ids, test_input_masks, test_segment_ids,test_imagesX],testY),shuffle= True, epochs=10, batch_size=512)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "# plt.plot(history.history['acc'])\n",
    "# plt.plot(history.history['val_acc'])\n",
    "# plt.title('model accuracy')\n",
    "# plt.ylabel('accuracy')\n",
    "# plt.xlabel('epoch')\n",
    "# plt.axhline(y=0.76,linewidth=1, color='r')\n",
    "# plt.legend(['train', 'val'], loc='upper left')\n",
    "# plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "# plt.plot(history.history['loss'])\n",
    "# plt.plot(history.history['val_loss'])\n",
    "# plt.title('model loss')\n",
    "# plt.ylabel('loss')\n",
    "# plt.xlabel('epoch')\n",
    "# # plt.ylim((0,1))\n",
    "# plt.legend(['train', 'val'], loc='upper left')\n",
    "# plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "# model.evaluate([test_input_ids, test_input_masks, test_segment_ids,test_imagesX],testY)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "# model.evaluate([train_input_ids, train_input_masks, train_segment_ids,train_imagesX],trainY)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python (FN)",
   "language": "python",
   "name": "fn"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
